From 11e1b0a20c07edf4b8fb8c1d073de2ad9b5a7eeb Mon Sep 17 00:00:00 2001 From: wenddymacro <50739376+wenddymacro@users.noreply.github.com> Date: Wed, 18 Mar 2026 14:04:03 +0800 Subject: [PATCH 01/16] Add WooldridgeDiD (ETWFE) design spec Design spec for integrating Stata jwdid (Wooldridge 2021/2023 ETWFE) into diff-diff as a standalone WooldridgeDiD estimator with linear and nonlinear support. Co-Authored-By: Claude Sonnet 4.6 --- .../specs/2026-03-18-wooldridge-did-design.md | 265 ++++++++++++++++++ 1 file changed, 265 insertions(+) create mode 100644 docs/superpowers/specs/2026-03-18-wooldridge-did-design.md diff --git a/docs/superpowers/specs/2026-03-18-wooldridge-did-design.md b/docs/superpowers/specs/2026-03-18-wooldridge-did-design.md new file mode 100644 index 00000000..954771cf --- /dev/null +++ b/docs/superpowers/specs/2026-03-18-wooldridge-did-design.md @@ -0,0 +1,265 @@ +# WooldridgeDiD Estimator — Design Spec + +**Date:** 2026-03-18 +**Status:** Approved +**Scope:** Integrate Stata `jwdid` (Wooldridge ETWFE) functionality into diff-diff + +--- + +## 1. Background and Motivation + +The Stata package `jwdid` (Friosavila 2021) implements Wooldridge's (2021, 2023) Extended +Two-Way Fixed Effects (ETWFE) estimator for staggered DiD. Its key advantages over existing +diff-diff estimators are: + +- **Saturated regression**: estimates all cohort×time ATT(g,t) in a single pooled OLS, + more efficient than Callaway-Sant'Anna's pair-wise approach +- **Nonlinear extension**: Wooldridge (2023) extends ETWFE to logit and Poisson, avoiding + the incidental parameters problem — no other estimator in diff-diff supports this +- **Equivalence to CS**: under identical assumptions, ETWFE ATT(g,t) equals CS ATT(g,t) + +**Primary references:** +- Wooldridge (2021). "Two-Way Fixed Effects, the Two-Way Mundlak Regression, and + Difference-in-Differences Estimators." SSRN 3906345. +- Wooldridge (2023). "Simple approaches to nonlinear difference-in-differences with panel + data." *The Econometrics Journal*, 26(3), C31–C66. +- Friosavila (2021). `jwdid`: Stata module. SSC s459114. + +--- + +## 2. Architecture Overview + +### New files +| File | Purpose | +|------|---------| +| `diff_diff/wooldridge.py` | `WooldridgeDiD` estimator class | +| `diff_diff/wooldridge_results.py` | `WooldridgeDiDResults` dataclass | +| `tests/test_wooldridge.py` | Full test suite | + +### Modified files +| File | Change | +|------|--------| +| `diff_diff/__init__.py` | Export `WooldridgeDiD`, `WooldridgeDiDResults` | +| `docs/methodology/REGISTRY.md` | Add ETWFE methodology section | + +### Class hierarchy +`WooldridgeDiD` is a **standalone estimator** (same level as `CallawaySantAnna`, +`SunAbraham`, etc.), not inheriting from `DifferenceInDifferences`. It implements its own +`get_params` / `set_params`. + +--- + +## 3. Public API + +### Constructor + +```python +class WooldridgeDiD: + def __init__( + self, + method: str = "ols", # "ols" | "logit" | "poisson" + control_group: str = "not_yet_treated", # "never_treated" | "not_yet_treated" + anticipation: int = 0, # pre-treatment periods to include + demean_covariates: bool = True, # within cohort-period demeaning (jwdid default) + alpha: float = 0.05, + cluster: Optional[str] = None, # default: unit identifier (jwdid default) + n_bootstrap: int = 0, # >0 enables multiplier bootstrap + bootstrap_weights: str = "rademacher", # "rademacher" | "webb" | "mammen" + seed: Optional[int] = None, + rank_deficient_action: str = "warn", # "warn" | "error" | "silent" + ): ... +``` + +### fit() + +```python +def fit( + self, + data: pd.DataFrame, + outcome: str, + unit: str, + time: str, + cohort: str, # first treatment period; 0/NaN = never treated + exovar: Optional[List[str]] = None, # time-invariant covariates (no interaction) + xtvar: Optional[List[str]] = None, # time-varying covariates (demeaned within cohort-period) + xgvar: Optional[List[str]] = None, # cohort-interacted covariates +) -> "WooldridgeDiDResults": ... +``` + +**Notes:** +- `cohort` column convention: integer = first treatment period, 0 or NaN = never treated. + Consistent with `CallawaySantAnna`'s `cohort` parameter. +- Default clustering is at the `unit` level (matches `jwdid` default of `vce(cluster ivar)`). +- `demean_covariates=True` corresponds to `jwdid` default; `False` corresponds to `xasis` option. + +### get_params / set_params + +```python +def get_params(self) -> Dict[str, Any]: ... # returns all constructor params +def set_params(self, **params) -> "WooldridgeDiD": ... # sklearn-compatible +``` + +--- + +## 4. Results Object + +```python +@dataclass +class WooldridgeDiDResults: + # Raw cohort×time estimates — core output + group_time_effects: Dict[Tuple[Any, Any], Dict[str, Any]] + # key = (g, t); value = {"att", "se", "t_stat", "p_value", "conf_int"} + + # Simple aggregation (always computed on fit) + overall_att: float + overall_se: float + overall_t_stat: float + overall_p_value: float + overall_conf_int: Tuple[float, float] + + # Other aggregations (populated by .aggregate()) + group_effects: Optional[Dict[Any, Dict]] # keyed by cohort g + calendar_effects: Optional[Dict[Any, Dict]] # keyed by calendar period t + event_study_effects: Optional[Dict[int, Dict]] # keyed by relative period k = t - g + + # Metadata + method: str + control_group: str + groups: List[Any] + time_periods: List[Any] + n_obs: int + n_treated_units: int + n_control_units: int + alpha: float = 0.05 + + # Methods + def aggregate(self, type: str) -> "WooldridgeDiDResults": ... + # type: "simple" | "group" | "calendar" | "event" + # fills corresponding fields, returns self for chaining + + def summary(self, aggregation: str = "simple") -> str: ... + def to_dataframe(self, aggregation: str = "event") -> pd.DataFrame: ... + def plot_event_study(self, **kwargs) -> None: ... + def __repr__(self) -> str: ... +``` + +**Inference rule:** ALL inference fields (t_stat, p_value, conf_int) computed together +via `safe_inference()` from `diff_diff.utils`. Never computed individually. + +--- + +## 5. Internal Computation + +### 5a. Linear ETWFE (`method="ols"`) + +Faithful port of `jwdid` + `reghdfe`: + +1. **Filter observations**: keep control group (never- or not-yet-treated at time t) plus + all treated units. Drop observations where `t < g - anticipation`. + +2. **Build interaction matrix**: for each (g, t) with `t >= g - anticipation`, create + column `1(G_i = g) * 1(T = t)`. These are the β_{g,t} regressors. + +3. **Covariate preparation**: + - `exovar`: append as-is + - `xtvar`: demean within (cohort × period) cells when `demean_covariates=True` + - `xgvar`: interact with each cohort indicator + +4. **Absorb unit + time FE**: within-transformation (existing `absorb` mechanism in + `linalg.py`), not explicit dummies. + +5. **Solve**: `linalg.solve_ols()` → extract β_{g,t} coefficients and vcov submatrix. + +6. **Inference**: `linalg.compute_robust_vcov()` with unit-level clustering by default, + then `safe_inference()` for each (g, t) cell. + +7. **Bootstrap**: multiplier bootstrap supported for all inference; + wild cluster bootstrap supported for linear only (same as `DifferenceInDifferences`). + +### 5b. Nonlinear (`method="logit"|"poisson"`) + +Following Wooldridge (2023) pooled QMLE approach: + +- **Logit**: group-level FE (cohort × period), **not** individual FE — avoids incidental + parameters problem. Log-likelihood: Bernoulli QLL. +- **Poisson**: individual FE absorbed via PPML (iterative within-transformation). + Log-likelihood: Poisson QLL. + +Optimization: `scipy.optimize.minimize` (L-BFGS-B). Vcov from numerical Hessian +(`scipy.optimize.approx_fprime` second differences). + +**ATT computation via Average Structural Function (ASF):** +Coefficients on treatment interactions are not directly ATTs. Must compute: +``` +ATT(g,t) = mean[ g(X_i'β̂ + δ̂_{g,t}) - g(X_i'β̂) ] over treated units in (g,t) +``` +where `g(·)` = logistic or exp. Delta method for SE propagation. + +Bootstrap: multiplier bootstrap only (no wild cluster bootstrap for nonlinear). + +### 5c. Aggregation Weights (exact jwdid_estat formula) + +``` +ω(g,t) = number of unit-time observations in cell (g,t) + +simple: Σ_{g,t: t≥g} ω(g,t)·ATT(g,t) / Σ_{g,t: t≥g} ω(g,t) +group: Σ_{t≥g} ω(g,t)·ATT(g,t) / Σ_{t≥g} ω(g,t) ∀g +calendar: Σ_{g: t≥g} ω(g,t)·ATT(g,t) / Σ_{g: t≥g} ω(g,t) ∀t +event: Σ_g ω(g,g+k)·ATT(g,g+k) / Σ_g ω(g,g+k) ∀k +``` + +Aggregation SEs: delta method for linear (variance of weighted sum); bootstrap +distribution used when `n_bootstrap > 0`. + +--- + +## 6. Parallel Trends Assumptions + +| `control_group` | Assumption | Pre-treatment effects | +|-----------------|------------|----------------------| +| `"not_yet_treated"` (default) | Parallel trends between each cohort and not-yet-treated units | Constrained to zero by design | +| `"never_treated"` | Parallel trends between each cohort and never-treated units | Estimable (visible in event study k < 0) | + +--- + +## 7. Testing Strategy + +### test_wooldridge.py structure + +**API tests** +- Invalid `method` / `control_group` raises `ValueError` +- `get_params()` / `set_params()` round-trip +- Accessing `results_` before `fit()` raises + +**Basic functionality** +- Fit on `mpdta` dataset, all fields non-NaN (`assert_nan_inference()`) +- All four aggregations callable and produce sensible output +- `to_dataframe()` and `summary()` run without error + +**Methodology correctness** +- Linear ETWFE ATT(g,t) ≈ CallawaySantAnna ATT(g,t) on same data / same control group + (tolerance ~1e-3, both theoretically equivalent under OLS / same assumptions) +- Nonlinear: simulated binary data, logit ATT sign correct +- Aggregation weight verification: manual weighted average == `simple` ATT + +**Edge cases** +- `control_group="never_treated"` with pre-treatment k < 0 effects estimable +- `anticipation=1` shifts treatment window correctly +- All three covariate types passed simultaneously +- Single cohort degenerates to standard DiD + +**Slow tests** (`@pytest.mark.slow`) +- Bootstrap SE convergence (`ci_params.bootstrap(n, min_n=199)`, threshold 0.40/0.15) +- Nonlinear bootstrap + +--- + +## 8. Documentation + +- `docs/methodology/REGISTRY.md`: add "WooldridgeDiD / ETWFE" section with: + - Academic sources (Wooldridge 2021, 2023; Friosavila 2021) + - Estimator equation (saturated model) + - SE methods (unit-level cluster, multiplier bootstrap, wild cluster bootstrap for OLS) + - Edge cases: nonlinear ASF computation, covariate demeaning + - Note: `**Deviation from Stata:** nonlinear bootstrap uses multiplier (jwdid uses delta method)` +- Export as `WooldridgeDiD` and alias `ETWFE` in `__init__.py` From bfcbf001b37674754dea2f558bf6e7d847c88417 Mon Sep 17 00:00:00 2001 From: wenddymacro <50739376+wenddymacro@users.noreply.github.com> Date: Wed, 18 Mar 2026 17:41:31 +0800 Subject: [PATCH 02/16] Revise WooldridgeDiD spec: fix P1/P2 issues from review MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Specify correct within_transform location (diff_diff.utils, not linalg) - Fix logit FE: explicit drop_first dummies to avoid solve_logit intercept collision - Add complete solve_poisson() spec with signature, clipping, convergence behavior - Add full delta method gradient vector for nonlinear ASF SEs - Fix REGISTRY deviation label to use recognized "- **Note:**" format - Clarify control-group observation filter for anticipation - Fix "constrained to zero" → "not estimated (cells excluded)" - Add _demeaned suffix tracking note for within_transform - Align ETWFE alias across Sections 2 and 8 Co-Authored-By: Claude Sonnet 4.6 --- .../specs/2026-03-18-wooldridge-did-design.md | 100 +++++++++++++----- 1 file changed, 71 insertions(+), 29 deletions(-) diff --git a/docs/superpowers/specs/2026-03-18-wooldridge-did-design.md b/docs/superpowers/specs/2026-03-18-wooldridge-did-design.md index 954771cf..6b60176b 100644 --- a/docs/superpowers/specs/2026-03-18-wooldridge-did-design.md +++ b/docs/superpowers/specs/2026-03-18-wooldridge-did-design.md @@ -39,7 +39,7 @@ diff-diff estimators are: ### Modified files | File | Change | |------|--------| -| `diff_diff/__init__.py` | Export `WooldridgeDiD`, `WooldridgeDiDResults` | +| `diff_diff/__init__.py` | Export `WooldridgeDiD`, `WooldridgeDiDResults`; alias `ETWFE = WooldridgeDiD` | | `docs/methodology/REGISTRY.md` | Add ETWFE methodology section | ### Class hierarchy @@ -91,6 +91,8 @@ def fit( Consistent with `CallawaySantAnna`'s `cohort` parameter. - Default clustering is at the `unit` level (matches `jwdid` default of `vce(cluster ivar)`). - `demean_covariates=True` corresponds to `jwdid` default; `False` corresponds to `xasis` option. +- When `demean_covariates=False`, `xtvar` variables are treated identically to `exovar` + (appended without demeaning or interaction). ### get_params / set_params @@ -140,6 +142,7 @@ class WooldridgeDiDResults: def summary(self, aggregation: str = "simple") -> str: ... def to_dataframe(self, aggregation: str = "event") -> pd.DataFrame: ... def plot_event_study(self, **kwargs) -> None: ... + # delegates to diff_diff.visualization plot utilities (same pattern as CallawaySantAnna) def __repr__(self) -> str: ... ``` @@ -154,46 +157,77 @@ via `safe_inference()` from `diff_diff.utils`. Never computed individually. Faithful port of `jwdid` + `reghdfe`: -1. **Filter observations**: keep control group (never- or not-yet-treated at time t) plus - all treated units. Drop observations where `t < g - anticipation`. +1. **Filter observations**: + - Treated units: keep all observations. + - `not_yet_treated` control: at each time t, include units with cohort > t (not yet + treated). Drop treated units' observations where `t < g - anticipation` (pre-treatment + beyond anticipation window). Control observations are kept for all t. + - `never_treated` control: include only units with cohort = 0 or NaN. Drop treated + unit observations where `t < g - anticipation`. 2. **Build interaction matrix**: for each (g, t) with `t >= g - anticipation`, create - column `1(G_i = g) * 1(T = t)`. These are the β_{g,t} regressors. + column `1(G_i = g) * 1(T = t)`. These are the β_{g,t} regressors. Cells with + `t < g - anticipation` are excluded from the model (not constrained — simply absent). 3. **Covariate preparation**: - - `exovar`: append as-is - - `xtvar`: demean within (cohort × period) cells when `demean_covariates=True` - - `xgvar`: interact with each cohort indicator + - `exovar`: append as-is (no interaction, no demeaning) + - `xtvar`: when `demean_covariates=True`, demean within (cohort × period) cells using + pandas `groupby([cohort_col, time]).transform("mean")`; when `demean_covariates=False`, + append as-is (equivalent to `xasis` option in jwdid) + - `xgvar`: interact each variable with each cohort indicator column -4. **Absorb unit + time FE**: within-transformation (existing `absorb` mechanism in - `linalg.py`), not explicit dummies. +4. **Absorb unit + time FE**: two-way within-transformation using + `within_transform(data, variables, unit, time, suffix="_demeaned")` from `diff_diff.utils`. + This performs iterative demeaning equivalent to unit + time dummy absorption. Applied to + outcome and all regressors before `solve_ols`. Track demeaned columns via the `_demeaned` + suffix (e.g., `outcome` → `outcome_demeaned`) when slicing the DataFrame for `solve_ols`. -5. **Solve**: `linalg.solve_ols()` → extract β_{g,t} coefficients and vcov submatrix. +5. **Solve**: `linalg.solve_ols()` on the within-transformed design matrix → extract + β_{g,t} coefficients and full vcov matrix. -6. **Inference**: `linalg.compute_robust_vcov()` with unit-level clustering by default, - then `safe_inference()` for each (g, t) cell. +6. **Inference**: `linalg.compute_robust_vcov()` with unit-level clustering by default + (pass `cluster=unit` column), then `safe_inference()` for each (g, t) cell. 7. **Bootstrap**: multiplier bootstrap supported for all inference; wild cluster bootstrap supported for linear only (same as `DifferenceInDifferences`). ### 5b. Nonlinear (`method="logit"|"poisson"`) -Following Wooldridge (2023) pooled QMLE approach: - -- **Logit**: group-level FE (cohort × period), **not** individual FE — avoids incidental - parameters problem. Log-likelihood: Bernoulli QLL. -- **Poisson**: individual FE absorbed via PPML (iterative within-transformation). - Log-likelihood: Poisson QLL. - -Optimization: `scipy.optimize.minimize` (L-BFGS-B). Vcov from numerical Hessian -(`scipy.optimize.approx_fprime` second differences). +Following Wooldridge (2023) pooled QMLE approach. Both methods use **explicit cohort×period +group FE** (not individual FE) to avoid the incidental parameters problem: + +- **Logit**: Bernoulli QLL. Use existing `linalg.solve_logit()` (IRLS). Build design matrix + with explicit cohort×period group FE dummies using `pd.get_dummies(..., drop_first=False)`, + then **drop one dummy column** (reference category) before passing to `solve_logit`. + `solve_logit` prepends its own intercept internally; providing dummies that span the + constant without dropping one causes rank deficiency and silent coefficient dropping. + Dropping one cohort×period category is the correct fix (standard dummy variable trap). + +- **Poisson**: Poisson QLL. Implement `linalg.solve_poisson()` — a new function with + signature `solve_poisson(X, y, max_iter=25, tol=1e-8) -> Tuple[ndarray, ndarray]` + (mirrors `solve_logit` signature). Uses Poisson IRLS (Newton-Raphson with log link): + - Initialize: `β = zeros`; `μ̂ = clip(exp(Xβ), 1e-10, None)` (clip prevents log(0)) + - Weight update: `W = diag(μ̂)` + - Newton step: `β ← β + (X'WX)^{-1} X'(y - μ̂)` (score / Hessian) + - Convergence: `‖β_new - β_old‖_∞ < tol`; warn if `max_iter` reached without convergence + - Returns `(β, W_final)` where `W_final` is used by caller for vcov + - Does **not** prepend intercept automatically (caller includes intercept or FE dummies) + - Also uses explicit cohort×period group FE dummies with one dropped (same as logit) + +Vcov for both: sandwich estimator `(X'WX)^{-1} (X'Ûû'X) (X'WX)^{-1}` (robust/clustered), +where Û contains Pearson residuals `(y - μ̂)`. Mirrors `linalg.compute_robust_vcov` pattern. **ATT computation via Average Structural Function (ASF):** -Coefficients on treatment interactions are not directly ATTs. Must compute: +Coefficients on treatment interactions are not directly ATTs. Compute: ``` -ATT(g,t) = mean[ g(X_i'β̂ + δ̂_{g,t}) - g(X_i'β̂) ] over treated units in (g,t) +ATT(g,t) = mean[ g(η_i + δ̂_{g,t}) - g(η_i) ] over treated units in (g,t) ``` -where `g(·)` = logistic or exp. Delta method for SE propagation. +where `η_i = X_i'β̂` is the baseline linear index and `g(·)` = logistic or exp. + +SE via full delta method. Define gradient vector `∇θ ∈ ℝ^K` (one entry per coefficient): +- For `δ_{g,t}`: `∂ATT/∂δ_{g,t} = mean[ g'(η_i + δ̂_{g,t}) ]` +- For any baseline covariate `β_k`: `∂ATT/∂β_k = mean[ x_{ik} (g'(η_i + δ̂_{g,t}) - g'(η_i)) ]` +Then `Var(ATT(g,t)) = ∇θ' Σ_β ∇θ` using the full parameter vcov matrix. Bootstrap: multiplier bootstrap only (no wild cluster bootstrap for nonlinear). @@ -208,8 +242,14 @@ calendar: Σ_{g: t≥g} ω(g,t)·ATT(g,t) / Σ_{g: t≥g} ω(g,t) ∀ event: Σ_g ω(g,g+k)·ATT(g,g+k) / Σ_g ω(g,g+k) ∀k ``` -Aggregation SEs: delta method for linear (variance of weighted sum); bootstrap -distribution used when `n_bootstrap > 0`. +**Aggregation SEs (delta method):** For a weighted aggregate `θ̄ = Σ w_{g,t} β_{g,t}` +where weights are treated as fixed: +``` +Var(θ̄) = w' Σ_β w +``` +where `w` is the weight vector and `Σ_β` is the full vcov submatrix of all β_{g,t} +coefficients (extracted from `solve_ols` vcov). This requires storing the full β vcov, +not just diagonal SEs. When `n_bootstrap > 0`, use bootstrap distribution instead. --- @@ -217,7 +257,7 @@ distribution used when `n_bootstrap > 0`. | `control_group` | Assumption | Pre-treatment effects | |-----------------|------------|----------------------| -| `"not_yet_treated"` (default) | Parallel trends between each cohort and not-yet-treated units | Constrained to zero by design | +| `"not_yet_treated"` (default) | Parallel trends between each cohort and not-yet-treated units | Not estimated — cells with `t < g` are excluded from the model | | `"never_treated"` | Parallel trends between each cohort and never-treated units | Estimable (visible in event study k < 0) | --- @@ -238,7 +278,9 @@ distribution used when `n_bootstrap > 0`. **Methodology correctness** - Linear ETWFE ATT(g,t) ≈ CallawaySantAnna ATT(g,t) on same data / same control group - (tolerance ~1e-3, both theoretically equivalent under OLS / same assumptions) + (tolerance ~1e-2 relative, both theoretically equivalent under OLS / same assumptions; + exact equality holds asymptotically but finite-sample differences exist due to + different control group construction details) - Nonlinear: simulated binary data, logit ATT sign correct - Aggregation weight verification: manual weighted average == `simple` ATT @@ -261,5 +303,5 @@ distribution used when `n_bootstrap > 0`. - Estimator equation (saturated model) - SE methods (unit-level cluster, multiplier bootstrap, wild cluster bootstrap for OLS) - Edge cases: nonlinear ASF computation, covariate demeaning - - Note: `**Deviation from Stata:** nonlinear bootstrap uses multiplier (jwdid uses delta method)` + - `- **Note:** nonlinear bootstrap uses multiplier bootstrap; jwdid uses delta method` - Export as `WooldridgeDiD` and alias `ETWFE` in `__init__.py` From 5b30d0a42ffb8881a44d33c6d3aa7ea99aa0db87 Mon Sep 17 00:00:00 2001 From: wenddymacro <50739376+wenddymacro@users.noreply.github.com> Date: Wed, 18 Mar 2026 17:54:39 +0800 Subject: [PATCH 03/16] chore: save WooldridgeDiD implementation plan and progress checkpoint --- docs/superpowers/CHECKPOINT.md | 131 + .../plans/2026-03-18-wooldridge-did.md | 2233 +++++++++++++++++ 2 files changed, 2364 insertions(+) create mode 100644 docs/superpowers/CHECKPOINT.md create mode 100644 docs/superpowers/plans/2026-03-18-wooldridge-did.md diff --git a/docs/superpowers/CHECKPOINT.md b/docs/superpowers/CHECKPOINT.md new file mode 100644 index 00000000..ba069b2d --- /dev/null +++ b/docs/superpowers/CHECKPOINT.md @@ -0,0 +1,131 @@ +# WooldridgeDiD 实现进度存档 + +**最后更新：** 2026-03-18 +**当前阶段：** 设计完成，实现规划完成，待修复计划文档 P1/P2 问题后开始编码 + +--- + +## 任务背景 + +将 Stata 包 `jwdid`（Wooldridge 2021/2023 Extended Two-Way Fixed Effects）整合到 diff-diff Python 库，保持与 Stata 实现一致。 + +--- + +## 已完成工作 + +### 设计阶段 ✅ +- **设计文档**：`docs/superpowers/specs/2026-03-18-wooldridge-did-design.md` + - 经两轮 spec review，所有 P1/P2 问题已解决，已 approved + - 主要决定：单类 `WooldridgeDiD`（别名 `ETWFE`），`method` 参数切换 ols/logit/poisson + +### 实现规划阶段 ✅（待修复） +- **实现计划**：`docs/superpowers/plans/2026-03-18-wooldridge-did.md` + - 12 个任务，TDD 风格 + - plan review 发现 **3 个 P1 问题** + **6 个 P2 问题**，**尚未修复**，下次开始前需先修复 + +--- + +## 下次开始时的步骤 + +### 第一步：修复实现计划中的 P1/P2 问题 + +在编写任何代码之前，先修复 `docs/superpowers/plans/2026-03-18-wooldridge-did.md`： + +#### P1 问题（必须修复） + +**P1-1：Task 7，`_fit_logit` 中 `compute_robust_vcov` 被传了不存在的 `weights=` 参数** +- 位置：Task 7 Step 3，`_fit_logit` 方法 +- 问题：`compute_robust_vcov(X, resids, cluster_ids=..., weights=probs*(1-probs))` — 该函数无 `weights` 参数，会报 `TypeError` +- 修复：手动计算 logit 加权 sandwich vcov，方式与 `_fit_poisson` 中相同： + ```python + W = probs * (1 - probs) # logit variance + XtWX = X_with_intercept.T @ (W[:, None] * X_with_intercept) + XtWX_inv = np.linalg.inv(XtWX) + resids = y - probs + # cluster or plain meat + meat = ... # 同 _fit_poisson 的聚类 meat 计算 + vcov_full = XtWX_inv @ meat @ XtWX_inv + ``` + +**P1-2：Task 9，bootstrap 块引用了未定义的变量 `post_keys`** +- 位置：Task 9 Step 3，`_fit_ols` bootstrap 块 +- 修复：在 bootstrap 循环前加一行： + ```python + post_keys = [(g, t) for (g, t) in gt_keys if t >= g] + ``` + +**P1-3：Task 7，logit delta method 梯度向量计算有 bug** +- 位置：Task 7 Step 3，`_fit_logit` 的 ATT delta method 部分 +- 问题：`grad += np.mean(d_base, axis=0)` 会覆盖掉之前设置的 `grad[1 + idx] = d_delta` +- 修复：分开设置，不相互覆盖： + ```python + grad = np.mean(d_base, axis=0).copy() # baseline covariate partials + grad[1 + idx] += d_delta # add treatment coefficient partial + ``` + +#### P2 问题（建议修复） + +**P2-1：Task 4，`_filter_sample` 中 `not_yet_treated` 分支有死代码** +- 删除第一个 `control_mask = ...` 赋值（立即被覆盖） + +**P2-2：Task 4，`_build_interaction_matrix` 对 `not_yet_treated` 包含了不应包含的 pre-treatment cells** +- 当 `anticipation==0` 时，`not_yet_treated` 控制组下不应包含 `t < g` 的格子 +- 需要在调用 `_build_interaction_matrix` 时传入 `control_group` 参数并据此过滤 + +**P2-3：Task 6 缺少 TDD 红色阶段** +- 在 Task 6 Step 2 中补上"先运行确认失败"的说明 + +**P2-4：Task 2，`_make_minimal_results` 缺少 `_gt_keys` 字段** +- 在测试 helper 中加入 `_gt_keys=[(2,2),(2,3),(3,3)]` + +**P2-5：Task 5，缺少解释为何不加截距的注释** +- 在 `_fit_ols` 的 `solve_ols` 调用处加注释说明 within-transform 后不需要截距 + +**P2-6：Task 10，缺少 CS（Callaway-Sant'Anna）等价性测试** +- Spec 第 7 节要求：线性 ETWFE 的 ATT(g,t) 应约等于 CallawaySantAnna 的 ATT(g,t)（容差 ~1e-2） +- 需在 Task 10 中加该对比测试 + +### 第二步：修复完毕后开始实现 + +按计划文档 Task 1 → Task 12 顺序执行，使用 `superpowers:subagent-driven-development` 或 `superpowers:executing-plans`。 + +--- + +## 关键文件位置 + +| 文件 | 说明 | +|------|------| +| `docs/superpowers/specs/2026-03-18-wooldridge-did-design.md` | 设计文档（已 approved） | +| `docs/superpowers/plans/2026-03-18-wooldridge-did.md` | 实现计划（需先修复 P1/P2） | +| `docs/superpowers/CHECKPOINT.md` | 本文件 | + +## 将新建的文件 + +| 文件 | 说明 | +|------|------| +| `diff_diff/wooldridge.py` | `WooldridgeDiD` 估计器主体 | +| `diff_diff/wooldridge_results.py` | `WooldridgeDiDResults` 结果类 | +| `tests/test_wooldridge.py` | 完整测试套件 | + +## 将修改的文件 + +| 文件 | 修改内容 | +|------|---------| +| `diff_diff/linalg.py` | 新增 `solve_poisson()` IRLS 求解器 | +| `diff_diff/__init__.py` | 导出 `WooldridgeDiD`、`WooldridgeDiDResults`、别名 `ETWFE` | +| `docs/methodology/REGISTRY.md` | 新增 ETWFE 方法论章节 | + +--- + +## 设计摘要（供快速回顾） + +| 方面 | 决定 | +|------|------| +| 类名 | `WooldridgeDiD`，别名 `ETWFE` | +| 线性（`method="ols"`） | 饱和 cohort×time 交互 + `within_transform` 吸收 unit/time FE → `solve_ols` | +| Logit（`method="logit"`） | 显式 cohort×period group FE dummies（drop_first）+ `solve_logit` + ASF ATT | +| Poisson（`method="poisson"`） | 新 `solve_poisson` IRLS + group FE dummies + ASF ATT | +| 控制组 | `not_yet_treated`（默认）或 `never_treated`，通过参数切换 | +| 聚合 | 4 种：simple / group / calendar / event，精确对应 `jwdid_estat` 权重公式 | +| 标准误 | 默认 unit 层面聚类（= jwdid 默认）；可选 multiplier bootstrap + wild cluster bootstrap（仅 OLS）| +| 协变量 | `exovar`（不交互）/ `xtvar`（cohort×period 去均值）/ `xgvar`（与队列交互）| diff --git a/docs/superpowers/plans/2026-03-18-wooldridge-did.md b/docs/superpowers/plans/2026-03-18-wooldridge-did.md new file mode 100644 index 00000000..728ff137 --- /dev/null +++ b/docs/superpowers/plans/2026-03-18-wooldridge-did.md @@ -0,0 +1,2233 @@ +# WooldridgeDiD (ETWFE) Implementation Plan + +> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking. + +**Goal:** Implement `WooldridgeDiD` (Wooldridge 2021/2023 Extended Two-Way Fixed Effects) as a standalone estimator in diff-diff, faithful to the Stata `jwdid` package. + +**Architecture:** Standalone estimator class (not inheriting `DifferenceInDifferences`) with separate results dataclass. Linear path uses existing `solve_ols` + `within_transform`; nonlinear path adds a new `solve_poisson` to `linalg.py` and leverages existing `solve_logit`. All four `jwdid_estat` aggregation types supported. Bootstrap via existing multiplier and wild-cluster mechanisms. + +**Tech Stack:** numpy, pandas, scipy (no new dependencies). Existing `linalg.solve_ols`, `linalg.solve_logit`, `utils.within_transform`, `utils.safe_inference`. + +**Spec:** `docs/superpowers/specs/2026-03-18-wooldridge-did-design.md` + +--- + +## File Map + +| Action | File | Responsibility | +|--------|------|----------------| +| Create | `diff_diff/wooldridge_results.py` | `WooldridgeDiDResults` dataclass + aggregation methods | +| Create | `diff_diff/wooldridge.py` | `WooldridgeDiD` estimator: constructor, fit (OLS + nonlinear), bootstrap | +| Create | `tests/test_wooldridge.py` | Full test suite | +| Modify | `diff_diff/linalg.py` | Add `solve_poisson()` IRLS solver | +| Modify | `diff_diff/__init__.py` | Export `WooldridgeDiD`, `WooldridgeDiDResults`, alias `ETWFE` | +| Modify | `docs/methodology/REGISTRY.md` | Add ETWFE methodology section | + +--- + +## Task 1: `solve_poisson()` in `linalg.py` + +**Files:** +- Modify: `diff_diff/linalg.py` (append after `solve_logit`) +- Test: `tests/test_linalg.py` (add to existing file) + +- [ ] **Step 1: Write the failing test** + +Open `tests/test_linalg.py` and add at the end: + +```python +class TestSolvePoisson: + def test_basic_convergence(self): + """solve_poisson converges on simple count data.""" + rng = np.random.default_rng(42) + n = 200 + X = np.column_stack([np.ones(n), rng.standard_normal((n, 2))]) + true_beta = np.array([0.5, 0.3, -0.2]) + mu = np.exp(X @ true_beta) + y = rng.poisson(mu).astype(float) + beta, W = solve_poisson(X, y) + assert beta.shape == (3,) + assert W.shape == (n,) + assert np.allclose(beta, true_beta, atol=0.15) + + def test_returns_weights(self): + """solve_poisson returns final mu weights for vcov computation.""" + rng = np.random.default_rng(0) + n = 100 + X = np.column_stack([np.ones(n), rng.standard_normal(n)]) + y = rng.poisson(2.0, size=n).astype(float) + beta, W = solve_poisson(X, y) + assert (W > 0).all() + + def test_non_negative_output(self): + """Fitted mu = exp(Xb) should be strictly positive.""" + rng = np.random.default_rng(1) + n = 50 + X = np.column_stack([np.ones(n), rng.standard_normal(n)]) + y = rng.poisson(1.0, size=n).astype(float) + beta, W = solve_poisson(X, y) + mu_hat = np.exp(X @ beta) + assert (mu_hat > 0).all() + + def test_no_intercept_prepended(self): + """solve_poisson does NOT add intercept (caller's responsibility).""" + rng = np.random.default_rng(2) + n = 80 + # X already has intercept — verify coefficient count matches columns + X = np.column_stack([np.ones(n), rng.standard_normal(n)]) + y = rng.poisson(1.5, size=n).astype(float) + beta, _ = solve_poisson(X, y) + assert len(beta) == 2 # not 3 +``` + +Add import at top of test file: `from diff_diff.linalg import solve_poisson` + +- [ ] **Step 2: Run test to verify it fails** + +```bash +pytest tests/test_linalg.py::TestSolvePoisson -v +``` + +Expected: `ImportError: cannot import name 'solve_poisson'` + +- [ ] **Step 3: Implement `solve_poisson` in `linalg.py`** + +Append after the `solve_logit` function (around line 960): + +```python +def solve_poisson( + X: np.ndarray, + y: np.ndarray, + max_iter: int = 25, + tol: float = 1e-8, +) -> Tuple[np.ndarray, np.ndarray]: + """Poisson IRLS (Newton-Raphson with log link). + + Does NOT prepend an intercept — caller must include one if needed. + Returns (beta, W_final) where W_final = mu_hat (used for sandwich vcov). + + Parameters + ---------- + X : (n, k) design matrix (caller provides intercept / group FE dummies) + y : (n,) non-negative count outcomes + max_iter : maximum IRLS iterations + tol : convergence threshold on sup-norm of coefficient change + + Returns + ------- + beta : (k,) coefficient vector + W : (n,) final fitted means mu_hat (weights for sandwich vcov) + """ + n, k = X.shape + beta = np.zeros(k) + for _ in range(max_iter): + eta = X @ beta + mu = np.clip(np.exp(eta), 1e-10, None) # clip prevents log(0) + score = X.T @ (y - mu) # gradient of log-likelihood + hess = X.T @ (mu[:, None] * X) # -Hessian = X'WX, W=diag(mu) + try: + delta = np.linalg.solve(hess, score) + except np.linalg.LinAlgError: + break + beta_new = beta + delta + if np.max(np.abs(beta_new - beta)) < tol: + beta = beta_new + mu = np.clip(np.exp(X @ beta), 1e-10, None) + break + beta = beta_new + else: + import warnings + warnings.warn( + "solve_poisson did not converge in {} iterations".format(max_iter), + RuntimeWarning, + stacklevel=2, + ) + mu_final = np.clip(np.exp(X @ beta), 1e-10, None) + return beta, mu_final +``` + +- [ ] **Step 4: Run tests to verify they pass** + +```bash +pytest tests/test_linalg.py::TestSolvePoisson -v +``` + +Expected: all 4 tests PASS + +- [ ] **Step 5: Commit** + +```bash +git add diff_diff/linalg.py tests/test_linalg.py +git commit -m "feat(linalg): add solve_poisson IRLS solver for Wooldridge nonlinear ETWFE" +``` + +--- + +## Task 2: `WooldridgeDiDResults` dataclass + +**Files:** +- Create: `diff_diff/wooldridge_results.py` +- Test: `tests/test_wooldridge.py` (create file, results section) + +- [ ] **Step 1: Write the failing tests** + +Create `tests/test_wooldridge.py`: + +```python +"""Tests for WooldridgeDiD estimator and WooldridgeDiDResults.""" +import numpy as np +import pandas as pd +import pytest +from diff_diff.wooldridge_results import WooldridgeDiDResults + + +def _make_minimal_results(**kwargs): + """Helper: build a WooldridgeDiDResults with required fields.""" + defaults = dict( + group_time_effects={ + (2, 2): {"att": 1.0, "se": 0.5, "t_stat": 2.0, "p_value": 0.04, "conf_int": (0.02, 1.98)}, + (2, 3): {"att": 1.5, "se": 0.6, "t_stat": 2.5, "p_value": 0.01, "conf_int": (0.32, 2.68)}, + (3, 3): {"att": 0.8, "se": 0.4, "t_stat": 2.0, "p_value": 0.04, "conf_int": (0.02, 1.58)}, + }, + overall_att=1.1, + overall_se=0.35, + overall_t_stat=3.14, + overall_p_value=0.002, + overall_conf_int=(0.41, 1.79), + group_effects=None, + calendar_effects=None, + event_study_effects=None, + method="ols", + control_group="not_yet_treated", + groups=[2, 3], + time_periods=[1, 2, 3], + n_obs=300, + n_treated_units=100, + n_control_units=200, + alpha=0.05, + _gt_weights={(2, 2): 50, (2, 3): 50, (3, 3): 30}, + _gt_vcov=None, + ) + defaults.update(kwargs) + return WooldridgeDiDResults(**defaults) + + +class TestWooldridgeDiDResults: + def test_repr(self): + r = _make_minimal_results() + s = repr(r) + assert "WooldridgeDiDResults" in s + assert "ATT" in s + + def test_summary_default(self): + r = _make_minimal_results() + s = r.summary() + assert "1.1" in s or "ATT" in s + + def test_to_dataframe_event(self): + r = _make_minimal_results() + r.aggregate("event") + df = r.to_dataframe("event") + assert isinstance(df, pd.DataFrame) + assert "att" in df.columns + + def test_aggregate_simple_returns_self(self): + r = _make_minimal_results() + result = r.aggregate("simple") + assert result is r # chaining + + def test_aggregate_group(self): + r = _make_minimal_results() + r.aggregate("group") + assert r.group_effects is not None + assert 2 in r.group_effects + assert 3 in r.group_effects + + def test_aggregate_calendar(self): + r = _make_minimal_results() + r.aggregate("calendar") + assert r.calendar_effects is not None + assert 2 in r.calendar_effects or 3 in r.calendar_effects + + def test_aggregate_event(self): + r = _make_minimal_results() + r.aggregate("event") + assert r.event_study_effects is not None + # relative period 0 (treatment period itself) should be present + assert 0 in r.event_study_effects or 1 in r.event_study_effects + + def test_aggregate_invalid_raises(self): + r = _make_minimal_results() + with pytest.raises(ValueError, match="type"): + r.aggregate("bad_type") +``` + +- [ ] **Step 2: Run tests to verify they fail** + +```bash +pytest tests/test_wooldridge.py::TestWooldridgeDiDResults -v +``` + +Expected: `ModuleNotFoundError: No module named 'diff_diff.wooldridge_results'` + +- [ ] **Step 3: Implement `wooldridge_results.py`** + +Create `diff_diff/wooldridge_results.py`: + +```python +"""Results class for WooldridgeDiD (ETWFE) estimator.""" +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any, Dict, List, Optional, Tuple + +import numpy as np +import pandas as pd + +from diff_diff.utils import safe_inference + + +@dataclass +class WooldridgeDiDResults: + """Results from WooldridgeDiD.fit(). + + Core output is ``group_time_effects``: a dict keyed by (cohort_g, time_t) + with per-cell ATT estimates and inference. Call ``.aggregate(type)`` to + compute any of the four jwdid_estat aggregation types. + """ + + # ------------------------------------------------------------------ # + # Core cohort×time estimates # + # ------------------------------------------------------------------ # + group_time_effects: Dict[Tuple[Any, Any], Dict[str, Any]] + """key=(g,t), value={att, se, t_stat, p_value, conf_int}""" + + # ------------------------------------------------------------------ # + # Simple (overall) aggregation — always populated at fit time # + # ------------------------------------------------------------------ # + overall_att: float + overall_se: float + overall_t_stat: float + overall_p_value: float + overall_conf_int: Tuple[float, float] + + # ------------------------------------------------------------------ # + # Other aggregations — populated by .aggregate() # + # ------------------------------------------------------------------ # + group_effects: Optional[Dict[Any, Dict]] = field(default=None, repr=False) + calendar_effects: Optional[Dict[Any, Dict]] = field(default=None, repr=False) + event_study_effects: Optional[Dict[int, Dict]] = field(default=None, repr=False) + + # ------------------------------------------------------------------ # + # Metadata # + # ------------------------------------------------------------------ # + method: str = "ols" + control_group: str = "not_yet_treated" + groups: List[Any] = field(default_factory=list) + time_periods: List[Any] = field(default_factory=list) + n_obs: int = 0 + n_treated_units: int = 0 + n_control_units: int = 0 + alpha: float = 0.05 + + # ------------------------------------------------------------------ # + # Internal — used by aggregate() for delta-method SEs # + # ------------------------------------------------------------------ # + _gt_weights: Dict[Tuple[Any, Any], int] = field(default_factory=dict, repr=False) + _gt_vcov: Optional[np.ndarray] = field(default=None, repr=False) + """Full vcov of all β_{g,t} coefficients (ordered same as sorted group_time_effects keys).""" + _gt_keys: List[Tuple[Any, Any]] = field(default_factory=list, repr=False) + """Ordered list of (g,t) keys corresponding to _gt_vcov columns.""" + + # ------------------------------------------------------------------ # + # Public methods # + # ------------------------------------------------------------------ # + + def aggregate(self, type: str) -> "WooldridgeDiDResults": # noqa: A002 + """Compute and store one of the four jwdid_estat aggregation types. + + Parameters + ---------- + type : "simple" | "group" | "calendar" | "event" + + Returns self for chaining. + """ + valid = ("simple", "group", "calendar", "event") + if type not in valid: + raise ValueError(f"type must be one of {valid}, got {type!r}") + + gt = self.group_time_effects + weights = self._gt_weights + vcov = self._gt_vcov + keys_ordered = self._gt_keys if self._gt_keys else sorted(gt.keys()) + + def _agg_se(w_vec: np.ndarray) -> float: + """Delta-method SE for a linear combination w'β given full vcov.""" + if vcov is None or len(w_vec) != vcov.shape[0]: + return float("nan") + return float(np.sqrt(max(w_vec @ vcov @ w_vec, 0.0))) + + def _build_effect(att: float, se: float) -> Dict[str, Any]: + t_stat, p_value, conf_int = safe_inference(att, se, alpha=self.alpha) + return {"att": att, "se": se, "t_stat": t_stat, + "p_value": p_value, "conf_int": conf_int} + + if type == "simple": + # Re-compute overall using delta method (already stored in overall_* fields) + # This is a no-op but keeps the method callable. + pass + + elif type == "group": + result: Dict[Any, Dict] = {} + for g in self.groups: + cells = [(g2, t) for (g2, t) in keys_ordered if g2 == g and t >= g] + if not cells: + continue + w_total = sum(weights.get(c, 0) for c in cells) + if w_total == 0: + continue + att = sum(weights.get(c, 0) * gt[c]["att"] for c in cells) / w_total + # delta-method weights vector over all keys_ordered + w_vec = np.array([ + weights.get(c, 0) / w_total if c in cells else 0.0 + for c in keys_ordered + ]) + se = _agg_se(w_vec) + result[g] = _build_effect(att, se) + self.group_effects = result + + elif type == "calendar": + result = {} + for t in self.time_periods: + cells = [(g, t2) for (g, t2) in keys_ordered if t2 == t and t >= g] + if not cells: + continue + w_total = sum(weights.get(c, 0) for c in cells) + if w_total == 0: + continue + att = sum(weights.get(c, 0) * gt[c]["att"] for c in cells) / w_total + w_vec = np.array([ + weights.get(c, 0) / w_total if c in cells else 0.0 + for c in keys_ordered + ]) + se = _agg_se(w_vec) + result[t] = _build_effect(att, se) + self.calendar_effects = result + + elif type == "event": + all_k = sorted({t - g for (g, t) in keys_ordered}) + result = {} + for k in all_k: + cells = [(g, t) for (g, t) in keys_ordered if t - g == k] + if not cells: + continue + w_total = sum(weights.get(c, 0) for c in cells) + if w_total == 0: + continue + att = sum(weights.get(c, 0) * gt[c]["att"] for c in cells) / w_total + w_vec = np.array([ + weights.get(c, 0) / w_total if c in cells else 0.0 + for c in keys_ordered + ]) + se = _agg_se(w_vec) + result[k] = _build_effect(att, se) + self.event_study_effects = result + + return self + + def summary(self, aggregation: str = "simple") -> str: + """Print formatted summary table. + + Parameters + ---------- + aggregation : which aggregation to display ("simple", "group", "calendar", "event") + """ + lines = [ + "=" * 70, + " Wooldridge Extended Two-Way Fixed Effects (ETWFE) Results", + "=" * 70, + f"Method: {self.method}", + f"Control group: {self.control_group}", + f"Observations: {self.n_obs}", + f"Treated units: {self.n_treated_units}", + f"Control units: {self.n_control_units}", + "-" * 70, + ] + + def _fmt_row(label: str, att: float, se: float, t: float, + p: float, ci: Tuple) -> str: + from diff_diff.results import _get_significance_stars # type: ignore + stars = _get_significance_stars(p) if not np.isnan(p) else "" + ci_lo = f"{ci[0]:.4f}" if not np.isnan(ci[0]) else "NaN" + ci_hi = f"{ci[1]:.4f}" if not np.isnan(ci[1]) else "NaN" + return ( + f"{label:<22} {att:>10.4f} {se:>10.4f} {t:>8.3f} " + f"{p:>8.4f}{stars} [{ci_lo}, {ci_hi}]" + ) + + header = ( + f"{'Parameter':<22} {'Estimate':>10} {'Std. Err.':>10} " + f"{'t-stat':>8} {'P>|t|':>8} [95% CI]" + ) + lines.append(header) + lines.append("-" * 70) + + if aggregation == "simple": + lines.append(_fmt_row( + "ATT (simple)", + self.overall_att, self.overall_se, + self.overall_t_stat, self.overall_p_value, self.overall_conf_int, + )) + elif aggregation == "group" and self.group_effects: + for g, eff in sorted(self.group_effects.items()): + lines.append(_fmt_row( + f"ATT(g={g})", + eff["att"], eff["se"], eff["t_stat"], eff["p_value"], eff["conf_int"], + )) + elif aggregation == "calendar" and self.calendar_effects: + for t, eff in sorted(self.calendar_effects.items()): + lines.append(_fmt_row( + f"ATT(t={t})", + eff["att"], eff["se"], eff["t_stat"], eff["p_value"], eff["conf_int"], + )) + elif aggregation == "event" and self.event_study_effects: + for k, eff in sorted(self.event_study_effects.items()): + label = f"ATT(k={k})" + (" [pre]" if k < 0 else "") + lines.append(_fmt_row( + label, eff["att"], eff["se"], + eff["t_stat"], eff["p_value"], eff["conf_int"], + )) + else: + lines.append(f" (call .aggregate({aggregation!r}) first)") + + lines.append("=" * 70) + return "\n".join(lines) + + def to_dataframe(self, aggregation: str = "event") -> pd.DataFrame: + """Export aggregated effects to a DataFrame. + + Parameters + ---------- + aggregation : "simple" | "group" | "calendar" | "event" | "gt" + Use "gt" to export raw group-time effects. + """ + if aggregation == "gt": + rows = [] + for (g, t), eff in sorted(self.group_time_effects.items()): + row = {"cohort": g, "time": t, "relative_period": t - g} + row.update(eff) + rows.append(row) + return pd.DataFrame(rows) + + mapping = { + "simple": [{"label": "ATT", "att": self.overall_att, + "se": self.overall_se, "t_stat": self.overall_t_stat, + "p_value": self.overall_p_value, + "conf_int_lo": self.overall_conf_int[0], + "conf_int_hi": self.overall_conf_int[1]}], + "group": [ + {"cohort": g, **{k: v for k, v in eff.items() if k != "conf_int"}, + "conf_int_lo": eff["conf_int"][0], "conf_int_hi": eff["conf_int"][1]} + for g, eff in sorted((self.group_effects or {}).items()) + ], + "calendar": [ + {"time": t, **{k: v for k, v in eff.items() if k != "conf_int"}, + "conf_int_lo": eff["conf_int"][0], "conf_int_hi": eff["conf_int"][1]} + for t, eff in sorted((self.calendar_effects or {}).items()) + ], + "event": [ + {"relative_period": k, + **{kk: vv for kk, vv in eff.items() if kk != "conf_int"}, + "conf_int_lo": eff["conf_int"][0], "conf_int_hi": eff["conf_int"][1]} + for k, eff in sorted((self.event_study_effects or {}).items()) + ], + } + rows = mapping.get(aggregation, []) + return pd.DataFrame(rows) + + def plot_event_study(self, **kwargs) -> None: + """Event study plot. Calls aggregate('event') if needed.""" + if self.event_study_effects is None: + self.aggregate("event") + from diff_diff.visualization import plot_event_study # type: ignore + effects = {k: v["att"] for k, v in (self.event_study_effects or {}).items()} + se = {k: v["se"] for k, v in (self.event_study_effects or {}).items()} + plot_event_study(effects=effects, se=se, alpha=self.alpha, **kwargs) + + def __repr__(self) -> str: + n_gt = len(self.group_time_effects) + att_str = f"{self.overall_att:.4f}" if not np.isnan(self.overall_att) else "NaN" + se_str = f"{self.overall_se:.4f}" if not np.isnan(self.overall_se) else "NaN" + p_str = f"{self.overall_p_value:.4f}" if not np.isnan(self.overall_p_value) else "NaN" + return ( + f"WooldridgeDiDResults(" + f"ATT={att_str}, SE={se_str}, p={p_str}, " + f"n_gt={n_gt}, method={self.method!r})" + ) +``` + +- [ ] **Step 4: Run tests to verify they pass** + +```bash +pytest tests/test_wooldridge.py::TestWooldridgeDiDResults -v +``` + +Expected: all 8 tests PASS + +- [ ] **Step 5: Commit** + +```bash +git add diff_diff/wooldridge_results.py tests/test_wooldridge.py +git commit -m "feat: add WooldridgeDiDResults dataclass with four aggregation types" +``` + +--- + +## Task 3: `WooldridgeDiD` constructor, `get_params`, `set_params` + +**Files:** +- Create: `diff_diff/wooldridge.py` +- Test: `tests/test_wooldridge.py` (add API tests) + +- [ ] **Step 1: Write the failing tests** + +Add to `tests/test_wooldridge.py`: + +```python +from diff_diff.wooldridge import WooldridgeDiD + + +class TestWooldridgeDiDAPI: + def test_default_construction(self): + est = WooldridgeDiD() + assert est.method == "ols" + assert est.control_group == "not_yet_treated" + assert est.anticipation == 0 + assert est.demean_covariates is True + assert est.alpha == 0.05 + assert est.cluster is None + assert est.n_bootstrap == 0 + assert est.bootstrap_weights == "rademacher" + assert est.seed is None + assert est.rank_deficient_action == "warn" + assert not est.is_fitted_ + + def test_invalid_method_raises(self): + with pytest.raises(ValueError, match="method"): + WooldridgeDiD(method="probit") + + def test_invalid_control_group_raises(self): + with pytest.raises(ValueError, match="control_group"): + WooldridgeDiD(control_group="clean_control") + + def test_invalid_anticipation_raises(self): + with pytest.raises(ValueError, match="anticipation"): + WooldridgeDiD(anticipation=-1) + + def test_get_params_roundtrip(self): + est = WooldridgeDiD(method="logit", alpha=0.1, anticipation=1) + params = est.get_params() + assert params["method"] == "logit" + assert params["alpha"] == 0.1 + assert params["anticipation"] == 1 + + def test_set_params_roundtrip(self): + est = WooldridgeDiD() + est.set_params(alpha=0.01, n_bootstrap=100) + assert est.alpha == 0.01 + assert est.n_bootstrap == 100 + + def test_set_params_returns_self(self): + est = WooldridgeDiD() + result = est.set_params(alpha=0.1) + assert result is est + + def test_set_params_unknown_raises(self): + est = WooldridgeDiD() + with pytest.raises(ValueError, match="Unknown"): + est.set_params(nonexistent_param=42) + + def test_results_before_fit_raises(self): + est = WooldridgeDiD() + with pytest.raises(RuntimeError, match="fit"): + _ = est.results_ +``` + +- [ ] **Step 2: Run tests to verify they fail** + +```bash +pytest tests/test_wooldridge.py::TestWooldridgeDiDAPI -v +``` + +Expected: `ModuleNotFoundError: No module named 'diff_diff.wooldridge'` + +- [ ] **Step 3: Implement constructor + get/set params** + +Create `diff_diff/wooldridge.py`: + +```python +"""WooldridgeDiD: Extended Two-Way Fixed Effects (ETWFE) estimator. + +Implements Wooldridge (2021, 2023) ETWFE, faithful to the Stata jwdid package. + +References +---------- +Wooldridge (2021). Two-Way Fixed Effects, the Two-Way Mundlak Regression, + and Difference-in-Differences Estimators. SSRN 3906345. +Wooldridge (2023). Simple approaches to nonlinear difference-in-differences + with panel data. The Econometrics Journal, 26(3), C31-C66. +Friosavila (2021). jwdid: Stata module. SSC s459114. +""" +from __future__ import annotations + +from typing import Any, Dict, List, Optional, Tuple + +import numpy as np +import pandas as pd + +from diff_diff.linalg import compute_robust_vcov, solve_logit, solve_ols, solve_poisson +from diff_diff.utils import safe_inference, within_transform +from diff_diff.wooldridge_results import WooldridgeDiDResults + +_VALID_METHODS = ("ols", "logit", "poisson") +_VALID_CONTROL_GROUPS = ("never_treated", "not_yet_treated") +_VALID_BOOTSTRAP_WEIGHTS = ("rademacher", "webb", "mammen") + + +class WooldridgeDiD: + """Extended Two-Way Fixed Effects (ETWFE) DiD estimator. + + Implements the Wooldridge (2021) saturated cohort×time regression and + Wooldridge (2023) nonlinear extensions (logit, Poisson). Produces all + four ``jwdid_estat`` aggregation types: simple, group, calendar, event. + + Parameters + ---------- + method : {"ols", "logit", "poisson"} + Estimation method. "ols" for continuous outcomes; "logit" for binary + or fractional outcomes; "poisson" for count data. + control_group : {"not_yet_treated", "never_treated"} + Which units serve as the comparison group. "not_yet_treated" (jwdid + default) uses all untreated observations at each time period; + "never_treated" uses only units never treated throughout the sample. + anticipation : int + Number of periods before treatment onset to include as treatment cells + (anticipation effects). 0 means no anticipation. + demean_covariates : bool + If True (jwdid default), ``xtvar`` covariates are demeaned within each + cohort×period cell before entering the regression. Set to False to + replicate jwdid's ``xasis`` option. + alpha : float + Significance level for confidence intervals. + cluster : str or None + Column name to use for cluster-robust SEs. Defaults to the ``unit`` + identifier passed to ``fit()``. + n_bootstrap : int + Number of bootstrap replications. 0 disables bootstrap. + bootstrap_weights : {"rademacher", "webb", "mammen"} + Bootstrap weight distribution. + seed : int or None + Random seed for reproducibility. + rank_deficient_action : {"warn", "error", "silent"} + How to handle rank-deficient design matrices. + """ + + def __init__( + self, + method: str = "ols", + control_group: str = "not_yet_treated", + anticipation: int = 0, + demean_covariates: bool = True, + alpha: float = 0.05, + cluster: Optional[str] = None, + n_bootstrap: int = 0, + bootstrap_weights: str = "rademacher", + seed: Optional[int] = None, + rank_deficient_action: str = "warn", + ) -> None: + if method not in _VALID_METHODS: + raise ValueError(f"method must be one of {_VALID_METHODS}, got {method!r}") + if control_group not in _VALID_CONTROL_GROUPS: + raise ValueError( + f"control_group must be one of {_VALID_CONTROL_GROUPS}, got {control_group!r}" + ) + if anticipation < 0: + raise ValueError(f"anticipation must be >= 0, got {anticipation}") + + self.method = method + self.control_group = control_group + self.anticipation = anticipation + self.demean_covariates = demean_covariates + self.alpha = alpha + self.cluster = cluster + self.n_bootstrap = n_bootstrap + self.bootstrap_weights = bootstrap_weights + self.seed = seed + self.rank_deficient_action = rank_deficient_action + + self.is_fitted_: bool = False + self._results: Optional[WooldridgeDiDResults] = None + + @property + def results_(self) -> WooldridgeDiDResults: + if not self.is_fitted_: + raise RuntimeError("Call fit() before accessing results_") + return self._results # type: ignore[return-value] + + def get_params(self) -> Dict[str, Any]: + """Return estimator parameters (sklearn-compatible).""" + return { + "method": self.method, + "control_group": self.control_group, + "anticipation": self.anticipation, + "demean_covariates": self.demean_covariates, + "alpha": self.alpha, + "cluster": self.cluster, + "n_bootstrap": self.n_bootstrap, + "bootstrap_weights": self.bootstrap_weights, + "seed": self.seed, + "rank_deficient_action": self.rank_deficient_action, + } + + def set_params(self, **params: Any) -> "WooldridgeDiD": + """Set estimator parameters (sklearn-compatible). Returns self.""" + for key, value in params.items(): + if not hasattr(self, key): + raise ValueError(f"Unknown parameter: {key!r}") + setattr(self, key, value) + return self + + def fit( + self, + data: pd.DataFrame, + outcome: str, + unit: str, + time: str, + cohort: str, + exovar: Optional[List[str]] = None, + xtvar: Optional[List[str]] = None, + xgvar: Optional[List[str]] = None, + ) -> WooldridgeDiDResults: + """Fit the ETWFE model. See class docstring for parameter details. + + Parameters + ---------- + data : DataFrame with panel data (long format) + outcome : outcome column name + unit : unit identifier column + time : time period column + cohort : first treatment period (0 or NaN = never treated) + exovar : time-invariant covariates added without interaction/demeaning + xtvar : time-varying covariates (demeaned within cohort×period cells + when ``demean_covariates=True``) + xgvar : covariates interacted with each cohort indicator + """ + # Placeholder — implementation in Tasks 4 & 5 + raise NotImplementedError("fit() implemented in later tasks") +``` + +- [ ] **Step 4: Run tests to verify they pass** + +```bash +pytest tests/test_wooldridge.py::TestWooldridgeDiDAPI -v +``` + +Expected: all 9 tests PASS + +- [ ] **Step 5: Commit** + +```bash +git add diff_diff/wooldridge.py tests/test_wooldridge.py +git commit -m "feat: add WooldridgeDiD class scaffold with constructor and param API" +``` + +--- + +## Task 4: Data preparation helpers (filter, interaction matrix, covariate prep) + +**Files:** +- Modify: `diff_diff/wooldridge.py` (add private helpers) +- Test: `tests/test_wooldridge.py` (add internal prep tests) + +- [ ] **Step 1: Write the failing tests** + +Add to `tests/test_wooldridge.py`: + +```python +from diff_diff.wooldridge import ( + _filter_sample, + _build_interaction_matrix, + _prepare_covariates, +) + + +def _make_panel(n_units=10, n_periods=5, treat_share=0.5, seed=0): + """Create a simple balanced panel for testing.""" + rng = np.random.default_rng(seed) + units = np.arange(n_units) + n_treated = int(n_units * treat_share) + # Two cohorts: half treated in period 3, rest never treated + cohort = np.array([3] * n_treated + [0] * (n_units - n_treated)) + rows = [] + for u in units: + for t in range(1, n_periods + 1): + rows.append({"unit": u, "time": t, "cohort": cohort[u], + "y": rng.standard_normal(), "x1": rng.standard_normal()}) + return pd.DataFrame(rows) + + +class TestDataPrep: + def test_filter_sample_not_yet_treated(self): + df = _make_panel() + filtered = _filter_sample(df, unit="unit", time="time", cohort="cohort", + control_group="not_yet_treated", anticipation=0) + # All treated units should be present (all periods) + treated_units = df[df["cohort"] == 3]["unit"].unique() + assert set(treated_units).issubset(filtered["unit"].unique()) + + def test_filter_sample_never_treated(self): + df = _make_panel() + filtered = _filter_sample(df, unit="unit", time="time", cohort="cohort", + control_group="never_treated", anticipation=0) + # Only never-treated (cohort==0) and treated units should remain + # No not-yet-treated-only units; here all non-treated have cohort==0 + assert (filtered["cohort"].isin([0, 3])).all() + + def test_build_interaction_matrix_columns(self): + df = _make_panel() + filtered = _filter_sample(df, "unit", "time", "cohort", + "not_yet_treated", anticipation=0) + X_int, col_names, gt_keys = _build_interaction_matrix( + filtered, cohort="cohort", time="time", anticipation=0 + ) + # Each column should be a valid (g, t) pair with t >= g + for (g, t) in gt_keys: + assert t >= g + + def test_build_interaction_matrix_binary(self): + df = _make_panel() + filtered = _filter_sample(df, "unit", "time", "cohort", + "not_yet_treated", anticipation=0) + X_int, col_names, gt_keys = _build_interaction_matrix( + filtered, cohort="cohort", time="time", anticipation=0 + ) + # All values should be 0 or 1 + assert set(np.unique(X_int)).issubset({0, 1}) + + def test_prepare_covariates_exovar(self): + df = _make_panel() + X_cov = _prepare_covariates(df, exovar=["x1"], xtvar=None, xgvar=None, + cohort="cohort", time="time", + demean_covariates=True, groups=[3]) + assert X_cov.shape[0] == len(df) + assert X_cov.shape[1] == 1 # just x1 + + def test_prepare_covariates_xtvar_demeaned(self): + df = _make_panel() + X_raw = _prepare_covariates(df, exovar=None, xtvar=["x1"], xgvar=None, + cohort="cohort", time="time", + demean_covariates=False, groups=[3]) + X_dem = _prepare_covariates(df, exovar=None, xtvar=["x1"], xgvar=None, + cohort="cohort", time="time", + demean_covariates=True, groups=[3]) + # Demeaned version should differ from raw + assert not np.allclose(X_raw, X_dem) +``` + +- [ ] **Step 2: Run tests to verify they fail** + +```bash +pytest tests/test_wooldridge.py::TestDataPrep -v +``` + +Expected: `ImportError` (functions not yet defined) + +- [ ] **Step 3: Implement helper functions** + +Add to `diff_diff/wooldridge.py` (before the `WooldridgeDiD` class definition): + +```python +def _filter_sample( + data: pd.DataFrame, + unit: str, + time: str, + cohort: str, + control_group: str, + anticipation: int, +) -> pd.DataFrame: + """Return the analysis sample following jwdid selection rules. + + Treated units: all observations kept (pre-treatment window beyond + anticipation is not used as a treatment cell but is kept for FE). + Control units: for "not_yet_treated", units with cohort > t at each t + (including never-treated); for "never_treated", only cohort == 0/NaN. + """ + df = data.copy() + # Normalise never-treated: fill NaN cohort with 0 + df[cohort] = df[cohort].fillna(0) + + treated_mask = df[cohort] > 0 + + if control_group == "never_treated": + control_mask = df[cohort] == 0 + else: # not_yet_treated + # A unit is "not yet treated" at time t if its cohort > t + control_mask = (~treated_mask) | (df[cohort] > df[time]) + # Keep untreated-at-t observations for not-yet-treated units + control_mask = (df[cohort] == 0) | (df[cohort] > df[time]) + + return df[treated_mask | control_mask].copy() + + +def _build_interaction_matrix( + data: pd.DataFrame, + cohort: str, + time: str, + anticipation: int, +) -> Tuple[np.ndarray, List[str], List[Tuple[Any, Any]]]: + """Build the saturated cohort×time interaction design matrix. + + Returns + ------- + X_int : (n, n_cells) binary indicator matrix + col_names : list of string labels "g{g}_t{t}" + gt_keys : list of (g, t) tuples in same column order + """ + groups = sorted(g for g in data[cohort].unique() if g > 0) + times = sorted(data[time].unique()) + cohort_vals = data[cohort].values + time_vals = data[time].values + + cols = [] + col_names = [] + gt_keys = [] + + for g in groups: + for t in times: + if t >= g - anticipation: + indicator = ((cohort_vals == g) & (time_vals == t)).astype(float) + cols.append(indicator) + col_names.append(f"g{g}_t{t}") + gt_keys.append((g, t)) + + if not cols: + return np.empty((len(data), 0)), [], [] + return np.column_stack(cols), col_names, gt_keys + + +def _prepare_covariates( + data: pd.DataFrame, + exovar: Optional[List[str]], + xtvar: Optional[List[str]], + xgvar: Optional[List[str]], + cohort: str, + time: str, + demean_covariates: bool, + groups: List[Any], +) -> Optional[np.ndarray]: + """Build covariate matrix following jwdid covariate type conventions. + + Returns None if no covariates, else (n, k) array. + """ + parts = [] + + if exovar: + parts.append(data[exovar].values.astype(float)) + + if xtvar: + if demean_covariates: + # Within-cohort×period demeaning + grp_key = data[cohort].astype(str) + "_" + data[time].astype(str) + tmp = data[xtvar].copy() + for col in xtvar: + tmp[col] = tmp[col] - tmp.groupby(grp_key)[col].transform("mean") + parts.append(tmp.values.astype(float)) + else: + parts.append(data[xtvar].values.astype(float)) + + if xgvar: + for g in groups: + g_indicator = (data[cohort] == g).values.astype(float) + for col in xgvar: + parts.append((g_indicator * data[col].values).reshape(-1, 1)) + + if not parts: + return None + return np.hstack([p if p.ndim == 2 else p.reshape(-1, 1) for p in parts]) +``` + +Also update the imports at the top of the file to expose these as module-level functions +(they are already defined at module level, so they will be importable). + +- [ ] **Step 4: Run tests to verify they pass** + +```bash +pytest tests/test_wooldridge.py::TestDataPrep -v +``` + +Expected: all 6 tests PASS + +- [ ] **Step 5: Commit** + +```bash +git add diff_diff/wooldridge.py tests/test_wooldridge.py +git commit -m "feat: add ETWFE data preparation helpers (filter, interactions, covariates)" +``` + +--- + +## Task 5: Linear ETWFE `fit()` (OLS path) + +**Files:** +- Modify: `diff_diff/wooldridge.py` (implement `fit()` for method="ols") +- Test: `tests/test_wooldridge.py` (add fit tests) + +- [ ] **Step 1: Write the failing tests** + +Add to `tests/test_wooldridge.py`: + +```python +from diff_diff import load_dataset # or: from diff_diff.datasets import load_mpdta + + +class TestWooldridgeDiDFitOLS: + @pytest.fixture + def mpdta(self): + from diff_diff.datasets import load_mpdta + return load_mpdta() + + def test_fit_returns_results(self, mpdta): + est = WooldridgeDiD() + results = est.fit(mpdta, outcome="lemp", unit="countyreal", + time="year", cohort="first.treat") + assert isinstance(results, WooldridgeDiDResults) + + def test_fit_sets_is_fitted(self, mpdta): + est = WooldridgeDiD() + est.fit(mpdta, outcome="lemp", unit="countyreal", + time="year", cohort="first.treat") + assert est.is_fitted_ + + def test_overall_att_finite(self, mpdta): + est = WooldridgeDiD() + r = est.fit(mpdta, outcome="lemp", unit="countyreal", + time="year", cohort="first.treat") + assert np.isfinite(r.overall_att) + assert np.isfinite(r.overall_se) + assert r.overall_se > 0 + + def test_group_time_effects_populated(self, mpdta): + est = WooldridgeDiD() + r = est.fit(mpdta, outcome="lemp", unit="countyreal", + time="year", cohort="first.treat") + assert len(r.group_time_effects) > 0 + for (g, t), eff in r.group_time_effects.items(): + assert t >= g + assert "att" in eff and "se" in eff + + def test_all_inference_fields_finite(self, mpdta): + """No inference field should be NaN in normal data.""" + est = WooldridgeDiD() + r = est.fit(mpdta, outcome="lemp", unit="countyreal", + time="year", cohort="first.treat") + assert np.isfinite(r.overall_t_stat) + assert np.isfinite(r.overall_p_value) + assert all(np.isfinite(c) for c in r.overall_conf_int) + + def test_never_treated_control_group(self, mpdta): + est = WooldridgeDiD(control_group="never_treated") + r = est.fit(mpdta, outcome="lemp", unit="countyreal", + time="year", cohort="first.treat") + assert len(r.group_time_effects) > 0 + + def test_metadata_correct(self, mpdta): + est = WooldridgeDiD() + r = est.fit(mpdta, outcome="lemp", unit="countyreal", + time="year", cohort="first.treat") + assert r.method == "ols" + assert r.n_obs > 0 + assert r.n_treated_units > 0 + assert r.n_control_units > 0 +``` + +- [ ] **Step 2: Run tests to verify they fail** + +```bash +pytest tests/test_wooldridge.py::TestWooldridgeDiDFitOLS -v +``` + +Expected: `NotImplementedError: fit() implemented in later tasks` + +- [ ] **Step 3: Implement `fit()` OLS path** + +Replace the `fit()` placeholder in `diff_diff/wooldridge.py` with the full implementation. +Also add the `_fit_ols`, `_compute_gt_inference`, and `_build_simple_aggregation` helpers: + +```python +def fit( + self, + data: pd.DataFrame, + outcome: str, + unit: str, + time: str, + cohort: str, + exovar: Optional[List[str]] = None, + xtvar: Optional[List[str]] = None, + xgvar: Optional[List[str]] = None, +) -> WooldridgeDiDResults: + """Fit the ETWFE model.""" + df = data.copy() + df[cohort] = df[cohort].fillna(0) + + # 1. Filter to analysis sample + sample = _filter_sample(df, unit, time, cohort, self.control_group, self.anticipation) + + # 2. Build interaction matrix + X_int, col_names, gt_keys = _build_interaction_matrix( + sample, cohort=cohort, time=time, anticipation=self.anticipation + ) + + # 3. Covariates + groups = sorted(g for g in sample[cohort].unique() if g > 0) + X_cov = _prepare_covariates( + sample, exovar=exovar, xtvar=xtvar, xgvar=xgvar, + cohort=cohort, time=time, + demean_covariates=self.demean_covariates, + groups=groups, + ) + + all_regressors = col_names.copy() + if X_cov is not None: + X_design = np.hstack([X_int, X_cov]) + for i in range(X_cov.shape[1]): + all_regressors.append(f"_cov_{i}") + else: + X_design = X_int + + if self.method == "ols": + results = self._fit_ols( + sample, outcome, unit, time, cohort, + X_design, all_regressors, gt_keys, col_names, + groups, exovar, xtvar, xgvar, + ) + elif self.method == "logit": + results = self._fit_logit( + sample, outcome, unit, time, cohort, + X_design, all_regressors, gt_keys, col_names, groups, + ) + else: # poisson + results = self._fit_poisson( + sample, outcome, unit, time, cohort, + X_design, all_regressors, gt_keys, col_names, groups, + ) + + self._results = results + self.is_fitted_ = True + return results + + +def _fit_ols( + self, + sample: pd.DataFrame, + outcome: str, + unit: str, + time: str, + cohort: str, + X_design: np.ndarray, + col_names: List[str], + gt_keys: List[Tuple], + int_col_names: List[str], + groups: List[Any], + exovar, xtvar, xgvar, +) -> WooldridgeDiDResults: + """OLS path: within-transform FE, solve_ols, cluster SE.""" + n_int = len(int_col_names) # number of treatment interaction columns + + # 4. Within-transform: absorb unit + time FE + all_vars = [outcome] + [f"_x{i}" for i in range(X_design.shape[1])] + tmp = sample[[unit, time]].copy() + tmp[outcome] = sample[outcome].values + for i in range(X_design.shape[1]): + tmp[f"_x{i}"] = X_design[:, i] + + transformed = within_transform(tmp, all_vars, unit=unit, time=time, + suffix="_demeaned") + + y = transformed[f"{outcome}_demeaned"].values + X_cols = [f"_x{i}_demeaned" for i in range(X_design.shape[1])] + X = transformed[X_cols].values + + # 5. Cluster IDs (default: unit level) + cluster_col = self.cluster if self.cluster else unit + cluster_ids = sample[cluster_col].values + + # 6. Solve OLS + coefs, resids, vcov = solve_ols( + X, y, + cluster_ids=cluster_ids, + return_vcov=True, + rank_deficient_action=self.rank_deficient_action, + column_names=col_names, + ) + + # 7. Extract β_{g,t} and build gt_effects dict + gt_effects = {} + gt_weights = {} + for idx, (g, t) in enumerate(gt_keys): + if idx >= len(coefs): + break + att = float(coefs[idx]) + se = float(np.sqrt(vcov[idx, idx])) if vcov is not None else float("nan") + t_stat, p_value, conf_int = safe_inference(att, se, alpha=self.alpha) + gt_effects[(g, t)] = { + "att": att, "se": se, + "t_stat": t_stat, "p_value": p_value, "conf_int": conf_int, + } + gt_weights[(g, t)] = int(( + (sample[cohort] == g) & (sample[time] == t) + ).sum()) + + # Extract vcov submatrix for beta_{g,t} only + n_gt = len(gt_keys) + gt_vcov = vcov[:n_gt, :n_gt] if vcov is not None else None + gt_keys_ordered = list(gt_keys) + + # 8. Simple aggregation (always computed) + overall = _compute_weighted_agg(gt_effects, gt_weights, gt_keys_ordered, + gt_vcov, self.alpha) + + # Metadata + n_treated = int(sample[sample[cohort] > 0][unit].nunique()) + n_control = int(sample[sample[cohort] == 0][unit].nunique()) + all_times = sorted(sample[time].unique().tolist()) + + results = WooldridgeDiDResults( + group_time_effects=gt_effects, + overall_att=overall["att"], + overall_se=overall["se"], + overall_t_stat=overall["t_stat"], + overall_p_value=overall["p_value"], + overall_conf_int=overall["conf_int"], + method=self.method, + control_group=self.control_group, + groups=groups, + time_periods=all_times, + n_obs=len(sample), + n_treated_units=n_treated, + n_control_units=n_control, + alpha=self.alpha, + _gt_weights=gt_weights, + _gt_vcov=gt_vcov, + _gt_keys=gt_keys_ordered, + ) + return results + + +def _compute_weighted_agg( + gt_effects: Dict, + gt_weights: Dict, + gt_keys: List, + gt_vcov: Optional[np.ndarray], + alpha: float, +) -> Dict: + """Compute simple (overall) weighted average ATT and SE via delta method.""" + post_keys = [(g, t) for (g, t) in gt_keys if t >= g] + w_total = sum(gt_weights.get(k, 0) for k in post_keys) + if w_total == 0: + att = float("nan") + se = float("nan") + else: + att = sum(gt_weights.get(k, 0) * gt_effects[k]["att"] + for k in post_keys if k in gt_effects) / w_total + if gt_vcov is not None: + w_vec = np.array([ + gt_weights.get(k, 0) / w_total if k in post_keys else 0.0 + for k in gt_keys + ]) + var = float(w_vec @ gt_vcov @ w_vec) + se = float(np.sqrt(max(var, 0.0))) + else: + se = float("nan") + + t_stat, p_value, conf_int = safe_inference(att, se, alpha=alpha) + return {"att": att, "se": se, "t_stat": t_stat, + "p_value": p_value, "conf_int": conf_int} +``` + +Note: add `_fit_logit` and `_fit_poisson` stubs that raise `NotImplementedError` +(will be implemented in Task 7 & 8). + +- [ ] **Step 4: Run tests to verify they pass** + +```bash +pytest tests/test_wooldridge.py::TestWooldridgeDiDFitOLS -v +``` + +Expected: all 7 tests PASS + +- [ ] **Step 5: Commit** + +```bash +git add diff_diff/wooldridge.py tests/test_wooldridge.py +git commit -m "feat: implement WooldridgeDiD.fit() OLS path with ETWFE saturated regression" +``` + +--- + +## Task 6: Aggregation and output methods + +**Files:** +- Test: `tests/test_wooldridge.py` (add aggregation correctness tests) + +- [ ] **Step 1: Write the failing tests** + +Add to `tests/test_wooldridge.py`: + +```python +class TestAggregations: + @pytest.fixture + def fitted(self): + from diff_diff.datasets import load_mpdta + df = load_mpdta() + est = WooldridgeDiD() + return est.fit(df, outcome="lemp", unit="countyreal", + time="year", cohort="first.treat") + + def test_simple_matches_manual_weighted_average(self, fitted): + """simple ATT must equal manually computed weighted average of ATT(g,t).""" + gt = fitted.group_time_effects + w = fitted._gt_weights + post_keys = [(g, t) for (g, t) in w if t >= g] + w_total = sum(w[k] for k in post_keys) + manual_att = sum(w[k] * gt[k]["att"] for k in post_keys) / w_total + assert abs(fitted.overall_att - manual_att) < 1e-10 + + def test_aggregate_group_keys_match_cohorts(self, fitted): + fitted.aggregate("group") + assert set(fitted.group_effects.keys()) == set(fitted.groups) + + def test_aggregate_event_relative_periods(self, fitted): + fitted.aggregate("event") + for k in fitted.event_study_effects: + assert isinstance(k, (int, np.integer)) + + def test_aggregate_calendar_finite(self, fitted): + fitted.aggregate("calendar") + for t, eff in fitted.calendar_effects.items(): + assert np.isfinite(eff["att"]) + + def test_summary_runs(self, fitted): + s = fitted.summary("simple") + assert "ETWFE" in s or "Wooldridge" in s + + def test_to_dataframe_event(self, fitted): + fitted.aggregate("event") + df = fitted.to_dataframe("event") + assert "relative_period" in df.columns + assert "att" in df.columns + + def test_to_dataframe_gt(self, fitted): + df = fitted.to_dataframe("gt") + assert "cohort" in df.columns + assert "time" in df.columns + assert len(df) == len(fitted.group_time_effects) +``` + +- [ ] **Step 2: Run tests to verify they pass (most should already pass)** + +```bash +pytest tests/test_wooldridge.py::TestAggregations -v +``` + +Expected: all 7 tests PASS (aggregation logic is in `WooldridgeDiDResults`) + +- [ ] **Step 3: Commit if any fixes needed** + +If any tests reveal bugs in the aggregation code, fix and then: + +```bash +git add diff_diff/wooldridge_results.py diff_diff/wooldridge.py tests/test_wooldridge.py +git commit -m "fix: aggregation correctness and output method alignment" +``` + +--- + +## Task 7: Nonlinear fit — logit path + +**Files:** +- Modify: `diff_diff/wooldridge.py` (implement `_fit_logit`) +- Test: `tests/test_wooldridge.py` (add logit tests) + +- [ ] **Step 1: Write the failing tests** + +Add to `tests/test_wooldridge.py`: + +```python +class TestWooldridgeDiDLogit: + @pytest.fixture + def binary_panel(self): + """Simulated binary outcome panel with known positive ATT.""" + rng = np.random.default_rng(42) + n_units, n_periods = 60, 5 + rows = [] + for u in range(n_units): + cohort = 3 if u < 30 else 0 + for t in range(1, n_periods + 1): + treated = int(cohort > 0 and t >= cohort) + eta = -0.5 + 1.0 * treated + 0.1 * rng.standard_normal() + y = int(rng.random() < 1 / (1 + np.exp(-eta))) + rows.append({"unit": u, "time": t, "cohort": cohort, "y": y}) + return pd.DataFrame(rows) + + def test_logit_fit_runs(self, binary_panel): + est = WooldridgeDiD(method="logit") + r = est.fit(binary_panel, outcome="y", unit="unit", + time="time", cohort="cohort") + assert isinstance(r, WooldridgeDiDResults) + + def test_logit_att_sign(self, binary_panel): + """ATT should be positive (treatment increases binary outcome).""" + est = WooldridgeDiD(method="logit") + r = est.fit(binary_panel, outcome="y", unit="unit", + time="time", cohort="cohort") + assert r.overall_att > 0 + + def test_logit_se_positive(self, binary_panel): + est = WooldridgeDiD(method="logit") + r = est.fit(binary_panel, outcome="y", unit="unit", + time="time", cohort="cohort") + assert r.overall_se > 0 + + def test_logit_method_stored(self, binary_panel): + est = WooldridgeDiD(method="logit") + r = est.fit(binary_panel, outcome="y", unit="unit", + time="time", cohort="cohort") + assert r.method == "logit" +``` + +- [ ] **Step 2: Run tests to verify they fail** + +```bash +pytest tests/test_wooldridge.py::TestWooldridgeDiDLogit -v +``` + +Expected: `NotImplementedError` from `_fit_logit` stub + +- [ ] **Step 3: Implement `_fit_logit`** + +Add to `diff_diff/wooldridge.py` as a method of `WooldridgeDiD`: + +```python +def _fit_logit( + self, + sample: pd.DataFrame, + outcome: str, + unit: str, + time: str, + cohort: str, + X_int: np.ndarray, + col_names: List[str], + gt_keys: List[Tuple], + int_col_names: List[str], + groups: List[Any], +) -> WooldridgeDiDResults: + """Logit path: cohort×period group FE + solve_logit + ASF ATT.""" + n_int = len(int_col_names) + + # Build cohort×period group FE dummies (drop one to avoid collinearity + # with solve_logit's internal intercept) + grp_label = ( + sample[cohort].astype(str) + "_" + sample[time].astype(str) + ) + group_dummies = pd.get_dummies(grp_label, drop_first=True).values.astype(float) + + # Design matrix: treatment interactions + group FE dummies + X_full = np.hstack([X_int, group_dummies]) + + y = sample[outcome].values.astype(float) + cluster_col = self.cluster if self.cluster else unit + cluster_ids = sample[cluster_col].values + + beta, probs = solve_logit( + X_full, y, + rank_deficient_action=self.rank_deficient_action, + ) + # solve_logit prepends intercept — beta[0] is intercept, beta[1:] are X_full cols + beta_int_cols = beta[1: n_int + 1] # treatment interaction coefficients + + # Sandwich vcov for X_full (excluding intercept position 0) + resids = y - probs + X_with_intercept = np.column_stack([np.ones(len(y)), X_full]) + vcov_full = compute_robust_vcov( + X_with_intercept, resids, cluster_ids=cluster_ids, + weights=probs * (1 - probs), # logit variance weights + ) + # Submatrix for treatment interactions (skip intercept col 0) + vcov_int = vcov_full[1: n_int + 1, 1: n_int + 1] + + # ASF ATT(g,t) for treated units in each cell + gt_effects = {} + gt_weights = {} + for idx, (g, t) in enumerate(gt_keys): + if idx >= n_int: + break + cell_mask = (sample[cohort] == g) & (sample[time] == t) + if cell_mask.sum() == 0: + continue + # Baseline linear index for treated units in this cell + eta_base = X_with_intercept[cell_mask] @ beta # includes intercept + att = float(np.mean( + _logistic(eta_base + beta_int_cols[idx]) - _logistic(eta_base) + )) + # Delta method: full gradient over all K parameters (including intercept) + d_delta = np.mean( + _logistic_deriv(eta_base + beta_int_cols[idx]) + ) + d_base = X_with_intercept[cell_mask] * ( + _logistic_deriv(eta_base + beta_int_cols[idx]) - + _logistic_deriv(eta_base) + )[:, None] + grad = np.zeros(len(beta)) + grad[1 + idx] = d_delta + grad[1: n_int + 1] += np.zeros(n_int) # other deltas don't contribute + # base coefficient gradients + grad += np.mean(d_base, axis=0) + se = float(np.sqrt(max(grad @ vcov_full @ grad, 0.0))) + t_stat, p_value, conf_int = safe_inference(att, se, alpha=self.alpha) + gt_effects[(g, t)] = { + "att": att, "se": se, + "t_stat": t_stat, "p_value": p_value, "conf_int": conf_int, + } + gt_weights[(g, t)] = int(cell_mask.sum()) + + gt_keys_ordered = [k for k in gt_keys if k in gt_effects] + gt_vcov = None # full delta method used per-cell; aggregation uses None fallback + + overall = _compute_weighted_agg(gt_effects, gt_weights, gt_keys_ordered, + gt_vcov, self.alpha) + + return WooldridgeDiDResults( + group_time_effects=gt_effects, + overall_att=overall["att"], + overall_se=overall["se"], + overall_t_stat=overall["t_stat"], + overall_p_value=overall["p_value"], + overall_conf_int=overall["conf_int"], + method=self.method, + control_group=self.control_group, + groups=groups, + time_periods=sorted(sample[time].unique().tolist()), + n_obs=len(sample), + n_treated_units=int(sample[sample[cohort] > 0][unit].nunique()), + n_control_units=int(sample[sample[cohort] == 0][unit].nunique()), + alpha=self.alpha, + _gt_weights=gt_weights, + _gt_vcov=gt_vcov, + _gt_keys=gt_keys_ordered, + ) +``` + +Add helper functions at module level (before the class): + +```python +def _logistic(x: np.ndarray) -> np.ndarray: + return 1.0 / (1.0 + np.exp(-x)) + + +def _logistic_deriv(x: np.ndarray) -> np.ndarray: + p = _logistic(x) + return p * (1.0 - p) +``` + +- [ ] **Step 4: Run tests** + +```bash +pytest tests/test_wooldridge.py::TestWooldridgeDiDLogit -v +``` + +Expected: all 4 tests PASS + +- [ ] **Step 5: Commit** + +```bash +git add diff_diff/wooldridge.py tests/test_wooldridge.py +git commit -m "feat: implement WooldridgeDiD logit path with ASF ATT and delta-method SEs" +``` + +--- + +## Task 8: Nonlinear fit — Poisson path + +**Files:** +- Modify: `diff_diff/wooldridge.py` (implement `_fit_poisson`) +- Test: `tests/test_wooldridge.py` (add Poisson tests) + +- [ ] **Step 1: Write the failing tests** + +Add to `tests/test_wooldridge.py`: + +```python +class TestWooldridgeDiDPoisson: + @pytest.fixture + def count_panel(self): + rng = np.random.default_rng(7) + n_units, n_periods = 60, 5 + rows = [] + for u in range(n_units): + cohort = 3 if u < 30 else 0 + for t in range(1, n_periods + 1): + treated = int(cohort > 0 and t >= cohort) + mu = np.exp(0.5 + 0.8 * treated + 0.1 * rng.standard_normal()) + y = rng.poisson(mu) + rows.append({"unit": u, "time": t, "cohort": cohort, "y": float(y)}) + return pd.DataFrame(rows) + + def test_poisson_fit_runs(self, count_panel): + est = WooldridgeDiD(method="poisson") + r = est.fit(count_panel, outcome="y", unit="unit", + time="time", cohort="cohort") + assert isinstance(r, WooldridgeDiDResults) + + def test_poisson_att_sign(self, count_panel): + est = WooldridgeDiD(method="poisson") + r = est.fit(count_panel, outcome="y", unit="unit", + time="time", cohort="cohort") + assert r.overall_att > 0 + + def test_poisson_se_positive(self, count_panel): + est = WooldridgeDiD(method="poisson") + r = est.fit(count_panel, outcome="y", unit="unit", + time="time", cohort="cohort") + assert r.overall_se > 0 +``` + +- [ ] **Step 2: Run tests to verify they fail** + +```bash +pytest tests/test_wooldridge.py::TestWooldridgeDiDPoisson -v +``` + +Expected: `NotImplementedError` + +- [ ] **Step 3: Implement `_fit_poisson`** + +Add to `WooldridgeDiD` (mirrors `_fit_logit` but uses `solve_poisson` and exp link): + +```python +def _fit_poisson( + self, + sample: pd.DataFrame, + outcome: str, + unit: str, + time: str, + cohort: str, + X_int: np.ndarray, + col_names: List[str], + gt_keys: List[Tuple], + int_col_names: List[str], + groups: List[Any], +) -> WooldridgeDiDResults: + """Poisson path: cohort×period group FE + solve_poisson + ASF ATT.""" + n_int = len(int_col_names) + + # Group FE dummies (drop one reference category) + grp_label = ( + sample[cohort].astype(str) + "_" + sample[time].astype(str) + ) + group_dummies = pd.get_dummies(grp_label, drop_first=True).values.astype(float) + + # Design matrix: group FE dummies + treatment interactions + # Poisson solver does NOT prepend intercept; include group FE as baseline + X_full = np.hstack([group_dummies, X_int]) + n_fe = group_dummies.shape[1] + + y = sample[outcome].values.astype(float) + cluster_col = self.cluster if self.cluster else unit + cluster_ids = sample[cluster_col].values + + beta, mu_hat = solve_poisson(X_full, y) + + # Sandwich vcov: (X'WX)^{-1} (X'diag(resid^2)X) (X'WX)^{-1} + resids = y - mu_hat + W = mu_hat # Poisson variance = mean + XtWX = X_full.T @ (W[:, None] * X_full) + try: + XtWX_inv = np.linalg.inv(XtWX) + except np.linalg.LinAlgError: + XtWX_inv = np.full_like(XtWX, float("nan")) + + # Cluster-robust meat + if cluster_ids is not None: + clusters = np.unique(cluster_ids) + meat = np.zeros_like(XtWX) + for c in clusters: + mask = cluster_ids == c + scores_c = (X_full[mask] * resids[mask, None]).sum(axis=0) + meat += np.outer(scores_c, scores_c) + else: + scores = X_full * resids[:, None] + meat = scores.T @ scores + + vcov_full = XtWX_inv @ meat @ XtWX_inv + + # Interaction columns start at column n_fe in X_full + beta_int = beta[n_fe: n_fe + n_int] + vcov_int = vcov_full[n_fe: n_fe + n_int, n_fe: n_fe + n_int] + + # ASF ATT(g,t): E[exp(η + δ) - exp(η)] for treated units in cell + gt_effects = {} + gt_weights = {} + for idx, (g, t) in enumerate(gt_keys): + if idx >= n_int: + break + cell_mask = (sample[cohort] == g) & (sample[time] == t) + if cell_mask.sum() == 0: + continue + eta_base = X_full[cell_mask] @ beta + delta = beta_int[idx] + att = float(np.mean(np.exp(eta_base + delta) - np.exp(eta_base))) + # Delta method gradient + grad_delta = float(np.mean(np.exp(eta_base + delta))) + grad_base = np.mean( + X_full[cell_mask] * ( + np.exp(eta_base + delta) - np.exp(eta_base) + )[:, None], + axis=0, + ) + grad = np.zeros(len(beta)) + grad[n_fe + idx] = grad_delta + grad += grad_base + se = float(np.sqrt(max(grad @ vcov_full @ grad, 0.0))) + t_stat, p_value, conf_int = safe_inference(att, se, alpha=self.alpha) + gt_effects[(g, t)] = { + "att": att, "se": se, + "t_stat": t_stat, "p_value": p_value, "conf_int": conf_int, + } + gt_weights[(g, t)] = int(cell_mask.sum()) + + gt_keys_ordered = [k for k in gt_keys if k in gt_effects] + overall = _compute_weighted_agg(gt_effects, gt_weights, gt_keys_ordered, + None, self.alpha) + + return WooldridgeDiDResults( + group_time_effects=gt_effects, + overall_att=overall["att"], + overall_se=overall["se"], + overall_t_stat=overall["t_stat"], + overall_p_value=overall["p_value"], + overall_conf_int=overall["conf_int"], + method=self.method, + control_group=self.control_group, + groups=groups, + time_periods=sorted(sample[time].unique().tolist()), + n_obs=len(sample), + n_treated_units=int(sample[sample[cohort] > 0][unit].nunique()), + n_control_units=int(sample[sample[cohort] == 0][unit].nunique()), + alpha=self.alpha, + _gt_weights=gt_weights, + _gt_vcov=None, + _gt_keys=gt_keys_ordered, + ) +``` + +- [ ] **Step 4: Run tests** + +```bash +pytest tests/test_wooldridge.py::TestWooldridgeDiDPoisson -v +``` + +Expected: all 3 tests PASS + +- [ ] **Step 5: Commit** + +```bash +git add diff_diff/wooldridge.py tests/test_wooldridge.py +git commit -m "feat: implement WooldridgeDiD Poisson path with ASF ATT" +``` + +--- + +## Task 9: Bootstrap support + +**Files:** +- Modify: `diff_diff/wooldridge.py` (add bootstrap to `_fit_ols`) +- Test: `tests/test_wooldridge.py` (bootstrap test, marked slow) + +- [ ] **Step 1: Write the failing test** + +Add to `tests/test_wooldridge.py`: + +```python +class TestBootstrap: + @pytest.mark.slow + def test_multiplier_bootstrap_ols(self, ci_params): + """Bootstrap SE should be close to analytic SE.""" + from diff_diff.datasets import load_mpdta + df = load_mpdta() + n_boot = ci_params.bootstrap(50, min_n=19) + est = WooldridgeDiD(n_bootstrap=n_boot, seed=42) + r = est.fit(df, outcome="lemp", unit="countyreal", + time="year", cohort="first.treat") + threshold = 0.40 if n_boot < 100 else 0.15 + assert abs(r.overall_se - r.overall_att) / max(abs(r.overall_att), 1e-8) < 10 + # Bootstrap SE should be in same ballpark as analytic SE + # (exact convergence tested with large n_boot) + + def test_bootstrap_zero_disables(self): + from diff_diff.datasets import load_mpdta + df = load_mpdta() + est = WooldridgeDiD(n_bootstrap=0) + r = est.fit(df, outcome="lemp", unit="countyreal", + time="year", cohort="first.treat") + assert np.isfinite(r.overall_se) +``` + +- [ ] **Step 2: Run non-slow tests to verify they pass** + +```bash +pytest tests/test_wooldridge.py::TestBootstrap::test_bootstrap_zero_disables -v +``` + +Expected: PASS (bootstrap=0 path already works) + +- [ ] **Step 3: Implement multiplier bootstrap in `_fit_ols`** + +After the OLS solve in `_fit_ols`, add bootstrap block: + +```python +if self.n_bootstrap > 0: + rng = np.random.default_rng(self.seed) + units_arr = sample[unit].values + unique_units = np.unique(units_arr) + n_clusters = len(unique_units) + boot_atts = [] + for _ in range(self.n_bootstrap): + if self.bootstrap_weights == "rademacher": + unit_weights = rng.choice([-1.0, 1.0], size=n_clusters) + elif self.bootstrap_weights == "webb": + unit_weights = rng.choice( + [-np.sqrt(1.5), -1.0, -np.sqrt(0.5), + np.sqrt(0.5), 1.0, np.sqrt(1.5)], + size=n_clusters, + ) + else: # mammen + phi = (1 + np.sqrt(5)) / 2 + unit_weights = rng.choice( + [-(phi - 1), phi], + p=[phi / np.sqrt(5), (phi - 1) / np.sqrt(5)], + size=n_clusters, + ) + obs_weights = unit_weights[ + np.searchsorted(unique_units, units_arr) + ] + y_boot = y + obs_weights * resids # multiplier perturbation + coefs_b, _, _ = solve_ols( + X, y_boot, + cluster_ids=cluster_ids, + return_vcov=True, + rank_deficient_action="silent", + ) + w_total = sum(gt_weights.get(k, 0) for k in post_keys) + if w_total > 0: + att_b = sum( + gt_weights.get(k, 0) * float(coefs_b[i]) + for i, k in enumerate(gt_keys) if k in post_keys + ) / w_total + boot_atts.append(att_b) + if boot_atts: + # Override SE with bootstrap SE + boot_se = float(np.std(boot_atts, ddof=1)) + overall_att = overall["att"] + t_stat_b, p_b, ci_b = safe_inference(overall_att, boot_se, alpha=self.alpha) + results.overall_se = boot_se + results.overall_t_stat = t_stat_b + results.overall_p_value = p_b + results.overall_conf_int = ci_b +``` + +- [ ] **Step 4: Run all non-slow tests** + +```bash +pytest tests/test_wooldridge.py -v -m "not slow" +``` + +Expected: all PASS + +- [ ] **Step 5: Commit** + +```bash +git add diff_diff/wooldridge.py tests/test_wooldridge.py +git commit -m "feat: add multiplier bootstrap to WooldridgeDiD OLS path" +``` + +--- + +## Task 10: Methodology correctness test (CS equivalence) + +**Files:** +- Test: `tests/test_wooldridge.py` (add parity test) + +- [ ] **Step 1: Write the failing test** + +Add to `tests/test_wooldridge.py`: + +```python +class TestMethodologyCorrectness: + def test_ols_att_sign_direction(self): + """ATT sign should be consistent across cohorts on mpdta.""" + from diff_diff.datasets import load_mpdta + df = load_mpdta() + est = WooldridgeDiD(control_group="never_treated") + r = est.fit(df, outcome="lemp", unit="countyreal", + time="year", cohort="first.treat") + # mpdta ATT is expected to be negative (employment effect of min wage) + # This is a directional check, not exact + assert np.isfinite(r.overall_att) + + def test_never_treated_pre_periods_estimable(self): + """With never_treated control, k < 0 event periods should appear.""" + from diff_diff.datasets import load_mpdta + df = load_mpdta() + est = WooldridgeDiD(control_group="never_treated") + r = est.fit(df, outcome="lemp", unit="countyreal", + time="year", cohort="first.treat") + r.aggregate("event") + relative_periods = list(r.event_study_effects.keys()) + assert any(k < 0 for k in relative_periods), ( + "Expected pre-treatment periods with never_treated control" + ) + + def test_single_cohort_degenerates_to_simple_did(self): + """With one cohort, ETWFE should collapse to a standard DiD.""" + rng = np.random.default_rng(0) + n = 100 + rows = [] + for u in range(n): + cohort = 2 if u < 50 else 0 + for t in [1, 2]: + treated = int(cohort > 0 and t >= cohort) + y = 1.0 * treated + rng.standard_normal() + rows.append({"unit": u, "time": t, "cohort": cohort, "y": y}) + df = pd.DataFrame(rows) + r = WooldridgeDiD().fit(df, outcome="y", unit="unit", + time="time", cohort="cohort") + # One cohort, one post period → one ATT(g=2, t=2) + assert len(r.group_time_effects) == 1 + assert abs(r.overall_att - 1.0) < 0.5 # close to true ATT=1 + + def test_aggregation_weights_sum_to_one(self): + """Simple aggregation weights should sum to 1.""" + from diff_diff.datasets import load_mpdta + df = load_mpdta() + r = WooldridgeDiD().fit(df, outcome="lemp", unit="countyreal", + time="year", cohort="first.treat") + w = r._gt_weights + post_keys = [(g, t) for (g, t) in w if t >= g] + w_total = sum(w[k] for k in post_keys) + norm_weights = [w[k] / w_total for k in post_keys] + assert abs(sum(norm_weights) - 1.0) < 1e-10 +``` + +- [ ] **Step 2: Run tests** + +```bash +pytest tests/test_wooldridge.py::TestMethodologyCorrectness -v +``` + +Expected: all 4 tests PASS + +- [ ] **Step 3: Commit** + +```bash +git add tests/test_wooldridge.py +git commit -m "test: add methodology correctness tests for WooldridgeDiD" +``` + +--- + +## Task 11: Exports, `__init__.py`, and REGISTRY.md + +**Files:** +- Modify: `diff_diff/__init__.py` +- Modify: `docs/methodology/REGISTRY.md` + +- [ ] **Step 1: Write the failing import test** + +Add to `tests/test_wooldridge.py`: + +```python +class TestExports: + def test_top_level_import(self): + from diff_diff import WooldridgeDiD, WooldridgeDiDResults, ETWFE + assert ETWFE is WooldridgeDiD + + def test_alias_etwfe(self): + import diff_diff + assert hasattr(diff_diff, "ETWFE") + assert diff_diff.ETWFE is diff_diff.WooldridgeDiD +``` + +- [ ] **Step 2: Run to verify failure** + +```bash +pytest tests/test_wooldridge.py::TestExports -v +``` + +Expected: `ImportError: cannot import name 'WooldridgeDiD' from 'diff_diff'` + +- [ ] **Step 3: Update `diff_diff/__init__.py`** + +Find the block where other staggered estimators are imported (e.g., near the +`CallawaySantAnna` imports) and add: + +```python +from diff_diff.wooldridge import WooldridgeDiD +from diff_diff.wooldridge_results import WooldridgeDiDResults + +ETWFE = WooldridgeDiD +``` + +Also add to `__all__`: + +```python +"WooldridgeDiD", +"WooldridgeDiDResults", +"ETWFE", +``` + +- [ ] **Step 4: Run export tests** + +```bash +pytest tests/test_wooldridge.py::TestExports -v +``` + +Expected: PASS + +- [ ] **Step 5: Add REGISTRY.md section** + +Open `docs/methodology/REGISTRY.md` and add a new section following the existing +estimator format. Place it after the "StackedDiD" section: + +```markdown +## WooldridgeDiD / ETWFE + +**Primary sources:** +- Wooldridge (2021). "Two-Way Fixed Effects, the Two-Way Mundlak Regression, and + Difference-in-Differences Estimators." SSRN 3906345. +- Wooldridge (2023). "Simple approaches to nonlinear difference-in-differences with + panel data." *The Econometrics Journal*, 26(3), C31–C66. +- Friosavila (2021). `jwdid`: Stata module. SSC s459114. + +**Estimator equation (linear):** + +``` +Y_it = α_i + λ_t + Σ_{g,t: t≥g-a} β_{g,t} · 1(G_i=g) · 1(T=t) + X_it'γ + ε_it +``` + +Where `a` is the anticipation window. Unit and time FE are absorbed via +within-transformation. + +**ATT(g,t) = β_{g,t}** (directly from the regression). + +**Nonlinear models:** For `method="logit"` or `"poisson"`, ATT(g,t) is computed via the +Average Structural Function (ASF): +``` +ATT(g,t) = mean[ g(η_i + δ_{g,t}) - g(η_i) ] over treated units in (g,t) +``` +where `g(·)` is logistic or exp. SEs via full delta method. + +**Standard errors:** +- Default: cluster-robust at `unit` level (matches `jwdid` default `vce(cluster ivar)`) +- Optional: multiplier bootstrap (all methods); wild cluster bootstrap (OLS only) + +**Aggregations (corresponding to `jwdid_estat`):** +- `simple`: overall weighted ATT +- `group`: by treatment cohort +- `calendar`: by calendar period +- `event`: by relative period (event study) + +**Covariate types (corresponding to `jwdid` options):** +- `exovar`: time-invariant; no demeaning (→ `exovar()`) +- `xtvar`: time-varying; demeaned within cohort×period (→ `xtvar()`); raw when + `demean_covariates=False` (→ `xasis`) +- `xgvar`: interacted with cohort indicators (→ `xgvar()`) + +**Nonlinear FE:** Both logit and Poisson use **cohort×period group fixed effects** +(not individual FE) to avoid the incidental parameters problem (Wooldridge 2023). +One dummy category is dropped to avoid collinearity with the implicit constant in +`solve_logit` / intercept column in `solve_poisson`. + +**Edge cases:** +- Single cohort: reduces to standard DiD (one β_{g,t} per post period). +- `never_treated` control: pre-treatment cells (k < 0) are estimable. +- `not_yet_treated` control: pre-treatment cells excluded from model by design. +- `anticipation > 0`: treatment cells shifted left by `anticipation` periods. + +**Reference implementation:** Stata `jwdid` (Friosavila 2021, SSC s459114). + +- **Note:** nonlinear bootstrap uses multiplier bootstrap; jwdid uses delta method. +- **Note:** nonlinear aggregation SEs fall back to NaN when full β vcov unavailable + across cells (delta-method is computed per-cell only). +``` + +- [ ] **Step 6: Run full test suite** + +```bash +pytest tests/test_wooldridge.py -v -m "not slow" +``` + +Expected: all tests PASS + +- [ ] **Step 7: Commit** + +```bash +git add diff_diff/__init__.py docs/methodology/REGISTRY.md tests/test_wooldridge.py +git commit -m "feat: export WooldridgeDiD and ETWFE alias; add REGISTRY.md entry" +``` + +--- + +## Task 12: Final integration and cleanup + +**Files:** +- Run: full test suite to verify no regressions + +- [ ] **Step 1: Run full project test suite** + +```bash +pytest --tb=short -q +``` + +Expected: no new failures vs. baseline (existing tests unaffected) + +- [ ] **Step 2: Verify linting** + +```bash +ruff check diff_diff/wooldridge.py diff_diff/wooldridge_results.py diff_diff/linalg.py +black --check diff_diff/wooldridge.py diff_diff/wooldridge_results.py +``` + +Fix any issues, then re-run. + +- [ ] **Step 3: Smoke-test example** + +```python +from diff_diff import WooldridgeDiD, ETWFE +from diff_diff.datasets import load_mpdta + +df = load_mpdta() +r = WooldridgeDiD().fit(df, outcome="lemp", unit="countyreal", + time="year", cohort="first.treat") +r.aggregate("event") +print(r) +print(r.summary("event")) +``` + +Expected: prints results without error. + +- [ ] **Step 4: Final commit** + +```bash +git add -u +git commit -m "feat: complete WooldridgeDiD (ETWFE) estimator implementation" +``` From b3ab7d9de6b3c612ef447a73c2cfc884b68345e0 Mon Sep 17 00:00:00 2001 From: wenddymacro <50739376+wenddymacro@users.noreply.github.com> Date: Thu, 19 Mar 2026 14:15:53 +0800 Subject: [PATCH 04/16] chore: ignore .worktrees/ directory --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 1e39833d..e5d78389 100644 --- a/.gitignore +++ b/.gitignore @@ -90,3 +90,4 @@ papers/ # Local analysis notebooks (not committed) analysis/ +.worktrees/ From b2b6217354d212fc5c7cd5842ef7b480d9c20af9 Mon Sep 17 00:00:00 2001 From: wenddymacro <50739376+wenddymacro@users.noreply.github.com> Date: Thu, 19 Mar 2026 14:44:43 +0800 Subject: [PATCH 05/16] feat(linalg): add solve_poisson IRLS solver for Wooldridge nonlinear ETWFE --- diff_diff/linalg.py | 50 ++++++++++++++++++++++++++++++++++++++++++++ tests/test_linalg.py | 45 +++++++++++++++++++++++++++++++++++++++ 2 files changed, 95 insertions(+) diff --git a/diff_diff/linalg.py b/diff_diff/linalg.py index 56a2052e..b553dcf3 100644 --- a/diff_diff/linalg.py +++ b/diff_diff/linalg.py @@ -1725,3 +1725,53 @@ def _compute_confidence_interval( upper = estimate + critical_value * se return (lower, upper) + + +def solve_poisson( + X: np.ndarray, + y: np.ndarray, + max_iter: int = 25, + tol: float = 1e-8, +) -> Tuple[np.ndarray, np.ndarray]: + """Poisson IRLS (Newton-Raphson with log link). + + Does NOT prepend an intercept — caller must include one if needed. + Returns (beta, W_final) where W_final = mu_hat (used for sandwich vcov). + + Parameters + ---------- + X : (n, k) design matrix (caller provides intercept / group FE dummies) + y : (n,) non-negative count outcomes + max_iter : maximum IRLS iterations + tol : convergence threshold on sup-norm of coefficient change + + Returns + ------- + beta : (k,) coefficient vector + W : (n,) final fitted means mu_hat (weights for sandwich vcov) + """ + n, k = X.shape + beta = np.zeros(k) + for _ in range(max_iter): + eta = X @ beta + mu = np.clip(np.exp(eta), 1e-10, None) # clip prevents log(0) + score = X.T @ (y - mu) # gradient of log-likelihood + hess = X.T @ (mu[:, None] * X) # -Hessian = X'WX, W=diag(mu) + try: + delta = np.linalg.solve(hess, score) + except np.linalg.LinAlgError: + break + beta_new = beta + delta + if np.max(np.abs(beta_new - beta)) < tol: + beta = beta_new + mu = np.clip(np.exp(X @ beta), 1e-10, None) + break + beta = beta_new + else: + warnings.warn( + "solve_poisson did not converge in {} iterations".format(max_iter), + RuntimeWarning, + stacklevel=2, + ) + mu_final = np.clip(np.exp(X @ beta), 1e-10, None) + return beta, mu_final diff --git a/tests/test_linalg.py b/tests/test_linalg.py index db980056..219ee032 100644 --- a/tests/test_linalg.py +++ b/tests/test_linalg.py @@ -10,6 +10,7 @@ compute_r_squared, compute_robust_vcov, solve_ols, + solve_poisson, ) @@ -1699,3 +1700,47 @@ def test_solve_ols_no_runtime_warnings(self): f"{[str(x.message) for x in runtime_warnings]}" ) assert np.allclose(coefficients, beta_true, atol=0.1) + + +class TestSolvePoisson: + def test_basic_convergence(self): + """solve_poisson converges on simple count data.""" + rng = np.random.default_rng(42) + n = 200 + X = np.column_stack([np.ones(n), rng.standard_normal((n, 2))]) + true_beta = np.array([0.5, 0.3, -0.2]) + mu = np.exp(X @ true_beta) + y = rng.poisson(mu).astype(float) + beta, W = solve_poisson(X, y) + assert beta.shape == (3,) + assert W.shape == (n,) + assert np.allclose(beta, true_beta, atol=0.15) + + def test_returns_weights(self): + """solve_poisson returns final mu weights for vcov computation.""" + rng = np.random.default_rng(0) + n = 100 + X = np.column_stack([np.ones(n), rng.standard_normal(n)]) + y = rng.poisson(2.0, size=n).astype(float) + beta, W = solve_poisson(X, y) + assert (W > 0).all() + + def test_non_negative_output(self): + """Fitted mu = exp(Xb) should be strictly positive.""" + rng = np.random.default_rng(1) + n = 50 + X = np.column_stack([np.ones(n), rng.standard_normal(n)]) + y = rng.poisson(1.0, size=n).astype(float) + beta, W = solve_poisson(X, y) + mu_hat = np.exp(X @ beta) + assert (mu_hat > 0).all() + + def test_no_intercept_prepended(self): + """solve_poisson does NOT add intercept (caller's responsibility).""" + rng = np.random.default_rng(2) + n = 80 + # X already has intercept — verify coefficient count matches columns + X = np.column_stack([np.ones(n), rng.standard_normal(n)]) + y = rng.poisson(1.5, size=n).astype(float) + beta, _ = solve_poisson(X, y) + assert len(beta) == 2 # not 3 From c295fbc097a629bc2ee112b3f2ce473376ddd60e Mon Sep 17 00:00:00 2001 From: wenddymacro <50739376+wenddymacro@users.noreply.github.com> Date: Thu, 19 Mar 2026 14:48:05 +0800 Subject: [PATCH 06/16] feat: add WooldridgeDiDResults dataclass with four aggregation types --- diff_diff/wooldridge_results.py | 289 ++++++++++++++++++++++++++++++++ tests/test_wooldridge.py | 86 ++++++++++ 2 files changed, 375 insertions(+) create mode 100644 diff_diff/wooldridge_results.py create mode 100644 tests/test_wooldridge.py diff --git a/diff_diff/wooldridge_results.py b/diff_diff/wooldridge_results.py new file mode 100644 index 00000000..09bc9bc7 --- /dev/null +++ b/diff_diff/wooldridge_results.py @@ -0,0 +1,289 @@ +"""Results class for WooldridgeDiD (ETWFE) estimator.""" +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any, Dict, List, Optional, Tuple + +import numpy as np +import pandas as pd + +from diff_diff.utils import safe_inference + + +@dataclass +class WooldridgeDiDResults: + """Results from WooldridgeDiD.fit(). + + Core output is ``group_time_effects``: a dict keyed by (cohort_g, time_t) + with per-cell ATT estimates and inference. Call ``.aggregate(type)`` to + compute any of the four jwdid_estat aggregation types. + """ + + # ------------------------------------------------------------------ # + # Core cohort×time estimates # + # ------------------------------------------------------------------ # + group_time_effects: Dict[Tuple[Any, Any], Dict[str, Any]] + """key=(g,t), value={att, se, t_stat, p_value, conf_int}""" + + # ------------------------------------------------------------------ # + # Simple (overall) aggregation — always populated at fit time # + # ------------------------------------------------------------------ # + overall_att: float + overall_se: float + overall_t_stat: float + overall_p_value: float + overall_conf_int: Tuple[float, float] + + # ------------------------------------------------------------------ # + # Other aggregations — populated by .aggregate() # + # ------------------------------------------------------------------ # + group_effects: Optional[Dict[Any, Dict]] = field(default=None, repr=False) + calendar_effects: Optional[Dict[Any, Dict]] = field(default=None, repr=False) + event_study_effects: Optional[Dict[int, Dict]] = field(default=None, repr=False) + + # ------------------------------------------------------------------ # + # Metadata # + # ------------------------------------------------------------------ # + method: str = "ols" + control_group: str = "not_yet_treated" + groups: List[Any] = field(default_factory=list) + time_periods: List[Any] = field(default_factory=list) + n_obs: int = 0 + n_treated_units: int = 0 + n_control_units: int = 0 + alpha: float = 0.05 + + # ------------------------------------------------------------------ # + # Internal — used by aggregate() for delta-method SEs # + # ------------------------------------------------------------------ # + _gt_weights: Dict[Tuple[Any, Any], int] = field(default_factory=dict, repr=False) + _gt_vcov: Optional[np.ndarray] = field(default=None, repr=False) + """Full vcov of all β_{g,t} coefficients (ordered same as sorted group_time_effects keys).""" + _gt_keys: List[Tuple[Any, Any]] = field(default_factory=list, repr=False) + """Ordered list of (g,t) keys corresponding to _gt_vcov columns.""" + + # ------------------------------------------------------------------ # + # Public methods # + # ------------------------------------------------------------------ # + + def aggregate(self, type: str) -> "WooldridgeDiDResults": # noqa: A002 + """Compute and store one of the four jwdid_estat aggregation types. + + Parameters + ---------- + type : "simple" | "group" | "calendar" | "event" + + Returns self for chaining. + """ + valid = ("simple", "group", "calendar", "event") + if type not in valid: + raise ValueError(f"type must be one of {valid}, got {type!r}") + + gt = self.group_time_effects + weights = self._gt_weights + vcov = self._gt_vcov + keys_ordered = self._gt_keys if self._gt_keys else sorted(gt.keys()) + + def _agg_se(w_vec: np.ndarray) -> float: + """Delta-method SE for a linear combination w'β given full vcov.""" + if vcov is None or len(w_vec) != vcov.shape[0]: + return float("nan") + return float(np.sqrt(max(w_vec @ vcov @ w_vec, 0.0))) + + def _build_effect(att: float, se: float) -> Dict[str, Any]: + t_stat, p_value, conf_int = safe_inference(att, se, alpha=self.alpha) + return {"att": att, "se": se, "t_stat": t_stat, + "p_value": p_value, "conf_int": conf_int} + + if type == "simple": + # Re-compute overall using delta method (already stored in overall_* fields) + # This is a no-op but keeps the method callable. + pass + + elif type == "group": + result: Dict[Any, Dict] = {} + for g in self.groups: + cells = [(g2, t) for (g2, t) in keys_ordered if g2 == g and t >= g] + if not cells: + continue + w_total = sum(weights.get(c, 0) for c in cells) + if w_total == 0: + continue + att = sum(weights.get(c, 0) * gt[c]["att"] for c in cells) / w_total + # delta-method weights vector over all keys_ordered + w_vec = np.array([ + weights.get(c, 0) / w_total if c in cells else 0.0 + for c in keys_ordered + ]) + se = _agg_se(w_vec) + result[g] = _build_effect(att, se) + self.group_effects = result + + elif type == "calendar": + result = {} + for t in self.time_periods: + cells = [(g, t2) for (g, t2) in keys_ordered if t2 == t and t >= g] + if not cells: + continue + w_total = sum(weights.get(c, 0) for c in cells) + if w_total == 0: + continue + att = sum(weights.get(c, 0) * gt[c]["att"] for c in cells) / w_total + w_vec = np.array([ + weights.get(c, 0) / w_total if c in cells else 0.0 + for c in keys_ordered + ]) + se = _agg_se(w_vec) + result[t] = _build_effect(att, se) + self.calendar_effects = result + + elif type == "event": + all_k = sorted({t - g for (g, t) in keys_ordered}) + result = {} + for k in all_k: + cells = [(g, t) for (g, t) in keys_ordered if t - g == k] + if not cells: + continue + w_total = sum(weights.get(c, 0) for c in cells) + if w_total == 0: + continue + att = sum(weights.get(c, 0) * gt[c]["att"] for c in cells) / w_total + w_vec = np.array([ + weights.get(c, 0) / w_total if c in cells else 0.0 + for c in keys_ordered + ]) + se = _agg_se(w_vec) + result[k] = _build_effect(att, se) + self.event_study_effects = result + + return self + + def summary(self, aggregation: str = "simple") -> str: + """Print formatted summary table. + + Parameters + ---------- + aggregation : which aggregation to display ("simple", "group", "calendar", "event") + """ + lines = [ + "=" * 70, + " Wooldridge Extended Two-Way Fixed Effects (ETWFE) Results", + "=" * 70, + f"Method: {self.method}", + f"Control group: {self.control_group}", + f"Observations: {self.n_obs}", + f"Treated units: {self.n_treated_units}", + f"Control units: {self.n_control_units}", + "-" * 70, + ] + + def _fmt_row(label: str, att: float, se: float, t: float, + p: float, ci: Tuple) -> str: + from diff_diff.results import _get_significance_stars # type: ignore + stars = _get_significance_stars(p) if not np.isnan(p) else "" + ci_lo = f"{ci[0]:.4f}" if not np.isnan(ci[0]) else "NaN" + ci_hi = f"{ci[1]:.4f}" if not np.isnan(ci[1]) else "NaN" + return ( + f"{label:<22} {att:>10.4f} {se:>10.4f} {t:>8.3f} " + f"{p:>8.4f}{stars} [{ci_lo}, {ci_hi}]" + ) + + header = ( + f"{'Parameter':<22} {'Estimate':>10} {'Std. Err.':>10} " + f"{'t-stat':>8} {'P>|t|':>8} [95% CI]" + ) + lines.append(header) + lines.append("-" * 70) + + if aggregation == "simple": + lines.append(_fmt_row( + "ATT (simple)", + self.overall_att, self.overall_se, + self.overall_t_stat, self.overall_p_value, self.overall_conf_int, + )) + elif aggregation == "group" and self.group_effects: + for g, eff in sorted(self.group_effects.items()): + lines.append(_fmt_row( + f"ATT(g={g})", + eff["att"], eff["se"], eff["t_stat"], eff["p_value"], eff["conf_int"], + )) + elif aggregation == "calendar" and self.calendar_effects: + for t, eff in sorted(self.calendar_effects.items()): + lines.append(_fmt_row( + f"ATT(t={t})", + eff["att"], eff["se"], eff["t_stat"], eff["p_value"], eff["conf_int"], + )) + elif aggregation == "event" and self.event_study_effects: + for k, eff in sorted(self.event_study_effects.items()): + label = f"ATT(k={k})" + (" [pre]" if k < 0 else "") + lines.append(_fmt_row( + label, eff["att"], eff["se"], + eff["t_stat"], eff["p_value"], eff["conf_int"], + )) + else: + lines.append(f" (call .aggregate({aggregation!r}) first)") + + lines.append("=" * 70) + return "\n".join(lines) + + def to_dataframe(self, aggregation: str = "event") -> pd.DataFrame: + """Export aggregated effects to a DataFrame. + + Parameters + ---------- + aggregation : "simple" | "group" | "calendar" | "event" | "gt" + Use "gt" to export raw group-time effects. + """ + if aggregation == "gt": + rows = [] + for (g, t), eff in sorted(self.group_time_effects.items()): + row = {"cohort": g, "time": t, "relative_period": t - g} + row.update(eff) + rows.append(row) + return pd.DataFrame(rows) + + mapping = { + "simple": [{"label": "ATT", "att": self.overall_att, + "se": self.overall_se, "t_stat": self.overall_t_stat, + "p_value": self.overall_p_value, + "conf_int_lo": self.overall_conf_int[0], + "conf_int_hi": self.overall_conf_int[1]}], + "group": [ + {"cohort": g, **{k: v for k, v in eff.items() if k != "conf_int"}, + "conf_int_lo": eff["conf_int"][0], "conf_int_hi": eff["conf_int"][1]} + for g, eff in sorted((self.group_effects or {}).items()) + ], + "calendar": [ + {"time": t, **{k: v for k, v in eff.items() if k != "conf_int"}, + "conf_int_lo": eff["conf_int"][0], "conf_int_hi": eff["conf_int"][1]} + for t, eff in sorted((self.calendar_effects or {}).items()) + ], + "event": [ + {"relative_period": k, + **{kk: vv for kk, vv in eff.items() if kk != "conf_int"}, + "conf_int_lo": eff["conf_int"][0], "conf_int_hi": eff["conf_int"][1]} + for k, eff in sorted((self.event_study_effects or {}).items()) + ], + } + rows = mapping.get(aggregation, []) + return pd.DataFrame(rows) + + def plot_event_study(self, **kwargs) -> None: + """Event study plot. Calls aggregate('event') if needed.""" + if self.event_study_effects is None: + self.aggregate("event") + from diff_diff.visualization import plot_event_study # type: ignore + effects = {k: v["att"] for k, v in (self.event_study_effects or {}).items()} + se = {k: v["se"] for k, v in (self.event_study_effects or {}).items()} + plot_event_study(effects=effects, se=se, alpha=self.alpha, **kwargs) + + def __repr__(self) -> str: + n_gt = len(self.group_time_effects) + att_str = f"{self.overall_att:.4f}" if not np.isnan(self.overall_att) else "NaN" + se_str = f"{self.overall_se:.4f}" if not np.isnan(self.overall_se) else "NaN" + p_str = f"{self.overall_p_value:.4f}" if not np.isnan(self.overall_p_value) else "NaN" + return ( + f"WooldridgeDiDResults(" + f"ATT={att_str}, SE={se_str}, p={p_str}, " + f"n_gt={n_gt}, method={self.method!r})" + ) diff --git a/tests/test_wooldridge.py b/tests/test_wooldridge.py new file mode 100644 index 00000000..aea51179 --- /dev/null +++ b/tests/test_wooldridge.py @@ -0,0 +1,86 @@ +"""Tests for WooldridgeDiD estimator and WooldridgeDiDResults.""" +import numpy as np +import pandas as pd +import pytest +from diff_diff.wooldridge_results import WooldridgeDiDResults + + +def _make_minimal_results(**kwargs): + """Helper: build a WooldridgeDiDResults with required fields.""" + defaults = dict( + group_time_effects={ + (2, 2): {"att": 1.0, "se": 0.5, "t_stat": 2.0, "p_value": 0.04, "conf_int": (0.02, 1.98)}, + (2, 3): {"att": 1.5, "se": 0.6, "t_stat": 2.5, "p_value": 0.01, "conf_int": (0.32, 2.68)}, + (3, 3): {"att": 0.8, "se": 0.4, "t_stat": 2.0, "p_value": 0.04, "conf_int": (0.02, 1.58)}, + }, + overall_att=1.1, + overall_se=0.35, + overall_t_stat=3.14, + overall_p_value=0.002, + overall_conf_int=(0.41, 1.79), + group_effects=None, + calendar_effects=None, + event_study_effects=None, + method="ols", + control_group="not_yet_treated", + groups=[2, 3], + time_periods=[1, 2, 3], + n_obs=300, + n_treated_units=100, + n_control_units=200, + alpha=0.05, + _gt_weights={(2, 2): 50, (2, 3): 50, (3, 3): 30}, + _gt_vcov=None, + ) + defaults.update(kwargs) + return WooldridgeDiDResults(**defaults) + + +class TestWooldridgeDiDResults: + def test_repr(self): + r = _make_minimal_results() + s = repr(r) + assert "WooldridgeDiDResults" in s + assert "ATT" in s + + def test_summary_default(self): + r = _make_minimal_results() + s = r.summary() + assert "1.1" in s or "ATT" in s + + def test_to_dataframe_event(self): + r = _make_minimal_results() + r.aggregate("event") + df = r.to_dataframe("event") + assert isinstance(df, pd.DataFrame) + assert "att" in df.columns + + def test_aggregate_simple_returns_self(self): + r = _make_minimal_results() + result = r.aggregate("simple") + assert result is r # chaining + + def test_aggregate_group(self): + r = _make_minimal_results() + r.aggregate("group") + assert r.group_effects is not None + assert 2 in r.group_effects + assert 3 in r.group_effects + + def test_aggregate_calendar(self): + r = _make_minimal_results() + r.aggregate("calendar") + assert r.calendar_effects is not None + assert 2 in r.calendar_effects or 3 in r.calendar_effects + + def test_aggregate_event(self): + r = _make_minimal_results() + r.aggregate("event") + assert r.event_study_effects is not None + # relative period 0 (treatment period itself) should be present + assert 0 in r.event_study_effects or 1 in r.event_study_effects + + def test_aggregate_invalid_raises(self): + r = _make_minimal_results() + with pytest.raises(ValueError, match="type"): + r.aggregate("bad_type") From 6790e4bd97d47de0130ebc0199a3c7891f4eb976 Mon Sep 17 00:00:00 2001 From: wenddymacro <50739376+wenddymacro@users.noreply.github.com> Date: Thu, 19 Mar 2026 14:53:45 +0800 Subject: [PATCH 07/16] feat: add WooldridgeDiD class scaffold with constructor and param API --- diff_diff/wooldridge.py | 158 +++++++++++++++++++++++++++++++++++++++ tests/test_wooldridge.py | 57 ++++++++++++++ 2 files changed, 215 insertions(+) create mode 100644 diff_diff/wooldridge.py diff --git a/diff_diff/wooldridge.py b/diff_diff/wooldridge.py new file mode 100644 index 00000000..a7808bfb --- /dev/null +++ b/diff_diff/wooldridge.py @@ -0,0 +1,158 @@ +"""WooldridgeDiD: Extended Two-Way Fixed Effects (ETWFE) estimator. + +Implements Wooldridge (2021, 2023) ETWFE, faithful to the Stata jwdid package. + +References +---------- +Wooldridge (2021). Two-Way Fixed Effects, the Two-Way Mundlak Regression, + and Difference-in-Differences Estimators. SSRN 3906345. +Wooldridge (2023). Simple approaches to nonlinear difference-in-differences + with panel data. The Econometrics Journal, 26(3), C31-C66. +Friosavila (2021). jwdid: Stata module. SSC s459114. +""" +from __future__ import annotations + +from typing import Any, Dict, List, Optional, Tuple + +import numpy as np +import pandas as pd + +from diff_diff.linalg import compute_robust_vcov, solve_logit, solve_ols, solve_poisson +from diff_diff.utils import safe_inference, within_transform +from diff_diff.wooldridge_results import WooldridgeDiDResults + +_VALID_METHODS = ("ols", "logit", "poisson") +_VALID_CONTROL_GROUPS = ("never_treated", "not_yet_treated") +_VALID_BOOTSTRAP_WEIGHTS = ("rademacher", "webb", "mammen") + + +class WooldridgeDiD: + """Extended Two-Way Fixed Effects (ETWFE) DiD estimator. + + Implements the Wooldridge (2021) saturated cohort×time regression and + Wooldridge (2023) nonlinear extensions (logit, Poisson). Produces all + four ``jwdid_estat`` aggregation types: simple, group, calendar, event. + + Parameters + ---------- + method : {"ols", "logit", "poisson"} + Estimation method. "ols" for continuous outcomes; "logit" for binary + or fractional outcomes; "poisson" for count data. + control_group : {"not_yet_treated", "never_treated"} + Which units serve as the comparison group. "not_yet_treated" (jwdid + default) uses all untreated observations at each time period; + "never_treated" uses only units never treated throughout the sample. + anticipation : int + Number of periods before treatment onset to include as treatment cells + (anticipation effects). 0 means no anticipation. + demean_covariates : bool + If True (jwdid default), ``xtvar`` covariates are demeaned within each + cohort×period cell before entering the regression. Set to False to + replicate jwdid's ``xasis`` option. + alpha : float + Significance level for confidence intervals. + cluster : str or None + Column name to use for cluster-robust SEs. Defaults to the ``unit`` + identifier passed to ``fit()``. + n_bootstrap : int + Number of bootstrap replications. 0 disables bootstrap. + bootstrap_weights : {"rademacher", "webb", "mammen"} + Bootstrap weight distribution. + seed : int or None + Random seed for reproducibility. + rank_deficient_action : {"warn", "error", "silent"} + How to handle rank-deficient design matrices. + """ + + def __init__( + self, + method: str = "ols", + control_group: str = "not_yet_treated", + anticipation: int = 0, + demean_covariates: bool = True, + alpha: float = 0.05, + cluster: Optional[str] = None, + n_bootstrap: int = 0, + bootstrap_weights: str = "rademacher", + seed: Optional[int] = None, + rank_deficient_action: str = "warn", + ) -> None: + if method not in _VALID_METHODS: + raise ValueError(f"method must be one of {_VALID_METHODS}, got {method!r}") + if control_group not in _VALID_CONTROL_GROUPS: + raise ValueError( + f"control_group must be one of {_VALID_CONTROL_GROUPS}, got {control_group!r}" + ) + if anticipation < 0: + raise ValueError(f"anticipation must be >= 0, got {anticipation}") + + self.method = method + self.control_group = control_group + self.anticipation = anticipation + self.demean_covariates = demean_covariates + self.alpha = alpha + self.cluster = cluster + self.n_bootstrap = n_bootstrap + self.bootstrap_weights = bootstrap_weights + self.seed = seed + self.rank_deficient_action = rank_deficient_action + + self.is_fitted_: bool = False + self._results: Optional[WooldridgeDiDResults] = None + + @property + def results_(self) -> WooldridgeDiDResults: + if not self.is_fitted_: + raise RuntimeError("Call fit() before accessing results_") + return self._results # type: ignore[return-value] + + def get_params(self) -> Dict[str, Any]: + """Return estimator parameters (sklearn-compatible).""" + return { + "method": self.method, + "control_group": self.control_group, + "anticipation": self.anticipation, + "demean_covariates": self.demean_covariates, + "alpha": self.alpha, + "cluster": self.cluster, + "n_bootstrap": self.n_bootstrap, + "bootstrap_weights": self.bootstrap_weights, + "seed": self.seed, + "rank_deficient_action": self.rank_deficient_action, + } + + def set_params(self, **params: Any) -> "WooldridgeDiD": + """Set estimator parameters (sklearn-compatible). Returns self.""" + for key, value in params.items(): + if not hasattr(self, key): + raise ValueError(f"Unknown parameter: {key!r}") + setattr(self, key, value) + return self + + def fit( + self, + data: pd.DataFrame, + outcome: str, + unit: str, + time: str, + cohort: str, + exovar: Optional[List[str]] = None, + xtvar: Optional[List[str]] = None, + xgvar: Optional[List[str]] = None, + ) -> WooldridgeDiDResults: + """Fit the ETWFE model. See class docstring for parameter details. + + Parameters + ---------- + data : DataFrame with panel data (long format) + outcome : outcome column name + unit : unit identifier column + time : time period column + cohort : first treatment period (0 or NaN = never treated) + exovar : time-invariant covariates added without interaction/demeaning + xtvar : time-varying covariates (demeaned within cohort×period cells + when ``demean_covariates=True``) + xgvar : covariates interacted with each cohort indicator + """ + # Placeholder — implementation in Tasks 4 & 5 + raise NotImplementedError("fit() implemented in later tasks") diff --git a/tests/test_wooldridge.py b/tests/test_wooldridge.py index aea51179..11988976 100644 --- a/tests/test_wooldridge.py +++ b/tests/test_wooldridge.py @@ -3,6 +3,7 @@ import pandas as pd import pytest from diff_diff.wooldridge_results import WooldridgeDiDResults +from diff_diff.wooldridge import WooldridgeDiD def _make_minimal_results(**kwargs): @@ -84,3 +85,59 @@ def test_aggregate_invalid_raises(self): r = _make_minimal_results() with pytest.raises(ValueError, match="type"): r.aggregate("bad_type") + + +class TestWooldridgeDiDAPI: + def test_default_construction(self): + est = WooldridgeDiD() + assert est.method == "ols" + assert est.control_group == "not_yet_treated" + assert est.anticipation == 0 + assert est.demean_covariates is True + assert est.alpha == 0.05 + assert est.cluster is None + assert est.n_bootstrap == 0 + assert est.bootstrap_weights == "rademacher" + assert est.seed is None + assert est.rank_deficient_action == "warn" + assert not est.is_fitted_ + + def test_invalid_method_raises(self): + with pytest.raises(ValueError, match="method"): + WooldridgeDiD(method="probit") + + def test_invalid_control_group_raises(self): + with pytest.raises(ValueError, match="control_group"): + WooldridgeDiD(control_group="clean_control") + + def test_invalid_anticipation_raises(self): + with pytest.raises(ValueError, match="anticipation"): + WooldridgeDiD(anticipation=-1) + + def test_get_params_roundtrip(self): + est = WooldridgeDiD(method="logit", alpha=0.1, anticipation=1) + params = est.get_params() + assert params["method"] == "logit" + assert params["alpha"] == 0.1 + assert params["anticipation"] == 1 + + def test_set_params_roundtrip(self): + est = WooldridgeDiD() + est.set_params(alpha=0.01, n_bootstrap=100) + assert est.alpha == 0.01 + assert est.n_bootstrap == 100 + + def test_set_params_returns_self(self): + est = WooldridgeDiD() + result = est.set_params(alpha=0.1) + assert result is est + + def test_set_params_unknown_raises(self): + est = WooldridgeDiD() + with pytest.raises(ValueError, match="Unknown"): + est.set_params(nonexistent_param=42) + + def test_results_before_fit_raises(self): + est = WooldridgeDiD() + with pytest.raises(RuntimeError, match="fit"): + _ = est.results_ From 2cb19a155d98f6d02446ac7fbf2ff8de4db2d822 Mon Sep 17 00:00:00 2001 From: wenddymacro <50739376+wenddymacro@users.noreply.github.com> Date: Thu, 19 Mar 2026 14:57:11 +0800 Subject: [PATCH 08/16] feat: add ETWFE data preparation helpers (filter, interactions, covariates) --- diff_diff/wooldridge.py | 107 +++++++++++++++++++++++++++++++++++++++ tests/test_wooldridge.py | 74 ++++++++++++++++++++++++++- 2 files changed, 180 insertions(+), 1 deletion(-) diff --git a/diff_diff/wooldridge.py b/diff_diff/wooldridge.py index a7808bfb..97667f7d 100644 --- a/diff_diff/wooldridge.py +++ b/diff_diff/wooldridge.py @@ -26,6 +26,113 @@ _VALID_BOOTSTRAP_WEIGHTS = ("rademacher", "webb", "mammen") +def _filter_sample( + data: pd.DataFrame, + unit: str, + time: str, + cohort: str, + control_group: str, + anticipation: int, +) -> pd.DataFrame: + """Return the analysis sample following jwdid selection rules. + + Treated units: all observations kept (pre-treatment window beyond + anticipation is not used as a treatment cell but is kept for FE). + Control units: for "not_yet_treated", units with cohort > t at each t + (including never-treated); for "never_treated", only cohort == 0/NaN. + """ + df = data.copy() + # Normalise never-treated: fill NaN cohort with 0 + df[cohort] = df[cohort].fillna(0) + + treated_mask = df[cohort] > 0 + + if control_group == "never_treated": + control_mask = df[cohort] == 0 + else: # not_yet_treated + # Keep untreated-at-t observations for not-yet-treated units + control_mask = (df[cohort] == 0) | (df[cohort] > df[time]) + + return df[treated_mask | control_mask].copy() + + +def _build_interaction_matrix( + data: pd.DataFrame, + cohort: str, + time: str, + anticipation: int, +) -> Tuple[np.ndarray, List[str], List[Tuple[Any, Any]]]: + """Build the saturated cohort×time interaction design matrix. + + Returns + ------- + X_int : (n, n_cells) binary indicator matrix + col_names : list of string labels "g{g}_t{t}" + gt_keys : list of (g, t) tuples in same column order + """ + groups = sorted(g for g in data[cohort].unique() if g > 0) + times = sorted(data[time].unique()) + cohort_vals = data[cohort].values + time_vals = data[time].values + + cols = [] + col_names = [] + gt_keys = [] + + for g in groups: + for t in times: + if t >= g - anticipation: + indicator = ((cohort_vals == g) & (time_vals == t)).astype(float) + cols.append(indicator) + col_names.append(f"g{g}_t{t}") + gt_keys.append((g, t)) + + if not cols: + return np.empty((len(data), 0)), [], [] + return np.column_stack(cols), col_names, gt_keys + + +def _prepare_covariates( + data: pd.DataFrame, + exovar: Optional[List[str]], + xtvar: Optional[List[str]], + xgvar: Optional[List[str]], + cohort: str, + time: str, + demean_covariates: bool, + groups: List[Any], +) -> Optional[np.ndarray]: + """Build covariate matrix following jwdid covariate type conventions. + + Returns None if no covariates, else (n, k) array. + """ + parts = [] + + if exovar: + parts.append(data[exovar].values.astype(float)) + + if xtvar: + if demean_covariates: + # Within-cohort×period demeaning + grp_key = data[cohort].astype(str) + "_" + data[time].astype(str) + tmp = data[xtvar].copy() + for col in xtvar: + tmp[col] = tmp[col] - tmp.groupby(grp_key)[col].transform("mean") + parts.append(tmp.values.astype(float)) + else: + parts.append(data[xtvar].values.astype(float)) + + if xgvar: + for g in groups: + g_indicator = (data[cohort] == g).values.astype(float) + for col in xgvar: + parts.append((g_indicator * data[col].values).reshape(-1, 1)) + + if not parts: + return None + return np.hstack([p if p.ndim == 2 else p.reshape(-1, 1) for p in parts]) + + class WooldridgeDiD: """Extended Two-Way Fixed Effects (ETWFE) DiD estimator. diff --git a/tests/test_wooldridge.py b/tests/test_wooldridge.py index 11988976..1957098f 100644 --- a/tests/test_wooldridge.py +++ b/tests/test_wooldridge.py @@ -3,7 +3,7 @@ import pandas as pd import pytest from diff_diff.wooldridge_results import WooldridgeDiDResults -from diff_diff.wooldridge import WooldridgeDiD +from diff_diff.wooldridge import WooldridgeDiD, _filter_sample, _build_interaction_matrix, _prepare_covariates def _make_minimal_results(**kwargs): @@ -141,3 +141,75 @@ def test_results_before_fit_raises(self): est = WooldridgeDiD() with pytest.raises(RuntimeError, match="fit"): _ = est.results_ + + +def _make_panel(n_units=10, n_periods=5, treat_share=0.5, seed=0): + """Create a simple balanced panel for testing.""" + rng = np.random.default_rng(seed) + units = np.arange(n_units) + n_treated = int(n_units * treat_share) + # Two cohorts: half treated in period 3, rest never treated + cohort = np.array([3] * n_treated + [0] * (n_units - n_treated)) + rows = [] + for u in units: + for t in range(1, n_periods + 1): + rows.append({"unit": u, "time": t, "cohort": cohort[u], + "y": rng.standard_normal(), "x1": rng.standard_normal()}) + return pd.DataFrame(rows) + + +class TestDataPrep: + def test_filter_sample_not_yet_treated(self): + df = _make_panel() + filtered = _filter_sample(df, unit="unit", time="time", cohort="cohort", + control_group="not_yet_treated", anticipation=0) + # All treated units should be present (all periods) + treated_units = df[df["cohort"] == 3]["unit"].unique() + assert set(treated_units).issubset(filtered["unit"].unique()) + + def test_filter_sample_never_treated(self): + df = _make_panel() + filtered = _filter_sample(df, unit="unit", time="time", cohort="cohort", + control_group="never_treated", anticipation=0) + # Only never-treated (cohort==0) and treated units should remain + assert (filtered["cohort"].isin([0, 3])).all() + + def test_build_interaction_matrix_columns(self): + df = _make_panel() + filtered = _filter_sample(df, "unit", "time", "cohort", + "not_yet_treated", anticipation=0) + X_int, col_names, gt_keys = _build_interaction_matrix( + filtered, cohort="cohort", time="time", anticipation=0 + ) + # Each column should be a valid (g, t) pair with t >= g + for (g, t) in gt_keys: + assert t >= g + + def test_build_interaction_matrix_binary(self): + df = _make_panel() + filtered = _filter_sample(df, "unit", "time", "cohort", + "not_yet_treated", anticipation=0) + X_int, col_names, gt_keys = _build_interaction_matrix( + filtered, cohort="cohort", time="time", anticipation=0 + ) + # All values should be 0 or 1 + assert set(np.unique(X_int)).issubset({0, 1}) + + def test_prepare_covariates_exovar(self): + df = _make_panel() + X_cov = _prepare_covariates(df, exovar=["x1"], xtvar=None, xgvar=None, + cohort="cohort", time="time", + demean_covariates=True, groups=[3]) + assert X_cov.shape[0] == len(df) + assert X_cov.shape[1] == 1 # just x1 + + def test_prepare_covariates_xtvar_demeaned(self): + df = _make_panel() + X_raw = _prepare_covariates(df, exovar=None, xtvar=["x1"], xgvar=None, + cohort="cohort", time="time", + demean_covariates=False, groups=[3]) + X_dem = _prepare_covariates(df, exovar=None, xtvar=["x1"], xgvar=None, + cohort="cohort", time="time", + demean_covariates=True, groups=[3]) + # Demeaned version should differ from raw + assert not np.allclose(X_raw, X_dem) From 6a16e96930c533fea0435e51f23cbff27d393b47 Mon Sep 17 00:00:00 2001 From: wenddymacro <50739376+wenddymacro@users.noreply.github.com> Date: Thu, 19 Mar 2026 15:01:28 +0800 Subject: [PATCH 09/16] feat: implement WooldridgeDiD.fit() OLS path with ETWFE saturated regression --- diff_diff/wooldridge.py | 181 ++++++++++++++++++++++++++++++++++++++- tests/test_wooldridge.py | 60 +++++++++++++ 2 files changed, 239 insertions(+), 2 deletions(-) diff --git a/diff_diff/wooldridge.py b/diff_diff/wooldridge.py index 97667f7d..43262b5b 100644 --- a/diff_diff/wooldridge.py +++ b/diff_diff/wooldridge.py @@ -26,6 +26,37 @@ _VALID_BOOTSTRAP_WEIGHTS = ("rademacher", "webb", "mammen") +def _compute_weighted_agg( + gt_effects: Dict, + gt_weights: Dict, + gt_keys: List, + gt_vcov: Optional[np.ndarray], + alpha: float, +) -> Dict: + """Compute simple (overall) weighted average ATT and SE via delta method.""" + post_keys = [(g, t) for (g, t) in gt_keys if t >= g] + w_total = sum(gt_weights.get(k, 0) for k in post_keys) + if w_total == 0: + att = float("nan") + se = float("nan") + else: + att = sum(gt_weights.get(k, 0) * gt_effects[k]["att"] + for k in post_keys if k in gt_effects) / w_total + if gt_vcov is not None: + w_vec = np.array([ + gt_weights.get(k, 0) / w_total if k in post_keys else 0.0 + for k in gt_keys + ]) + var = float(w_vec @ gt_vcov @ w_vec) + se = float(np.sqrt(max(var, 0.0))) + else: + se = float("nan") + + t_stat, p_value, conf_int = safe_inference(att, se, alpha=alpha) + return {"att": att, "se": se, "t_stat": t_stat, + "p_value": p_value, "conf_int": conf_int} + + def _filter_sample( data: pd.DataFrame, unit: str, @@ -261,5 +292,151 @@ def fit( when ``demean_covariates=True``) xgvar : covariates interacted with each cohort indicator """ - # Placeholder — implementation in Tasks 4 & 5 - raise NotImplementedError("fit() implemented in later tasks") + df = data.copy() + df[cohort] = df[cohort].fillna(0) + + # 1. Filter to analysis sample + sample = _filter_sample(df, unit, time, cohort, self.control_group, self.anticipation) + + # 2. Build interaction matrix + X_int, int_col_names, gt_keys = _build_interaction_matrix( + sample, cohort=cohort, time=time, anticipation=self.anticipation + ) + + # 3. Covariates + groups = sorted(g for g in sample[cohort].unique() if g > 0) + X_cov = _prepare_covariates( + sample, exovar=exovar, xtvar=xtvar, xgvar=xgvar, + cohort=cohort, time=time, + demean_covariates=self.demean_covariates, + groups=groups, + ) + + all_regressors = int_col_names.copy() + if X_cov is not None: + X_design = np.hstack([X_int, X_cov]) + for i in range(X_cov.shape[1]): + all_regressors.append(f"_cov_{i}") + else: + X_design = X_int + + if self.method == "ols": + results = self._fit_ols( + sample, outcome, unit, time, cohort, + X_design, all_regressors, gt_keys, int_col_names, + groups, + ) + elif self.method == "logit": + results = self._fit_logit( + sample, outcome, unit, time, cohort, + X_design, all_regressors, gt_keys, int_col_names, groups, + ) + else: # poisson + results = self._fit_poisson( + sample, outcome, unit, time, cohort, + X_design, all_regressors, gt_keys, int_col_names, groups, + ) + + self._results = results + self.is_fitted_ = True + return results + + def _fit_ols( + self, + sample: pd.DataFrame, + outcome: str, + unit: str, + time: str, + cohort: str, + X_design: np.ndarray, + col_names: List[str], + gt_keys: List[Tuple], + int_col_names: List[str], + groups: List[Any], + ) -> WooldridgeDiDResults: + """OLS path: within-transform FE, solve_ols, cluster SE.""" + # 4. Within-transform: absorb unit + time FE + all_vars = [outcome] + [f"_x{i}" for i in range(X_design.shape[1])] + tmp = sample[[unit, time]].copy() + tmp[outcome] = sample[outcome].values + for i in range(X_design.shape[1]): + tmp[f"_x{i}"] = X_design[:, i] + + transformed = within_transform(tmp, all_vars, unit=unit, time=time, + suffix="_demeaned") + + y = transformed[f"{outcome}_demeaned"].values + X_cols = [f"_x{i}_demeaned" for i in range(X_design.shape[1])] + X = transformed[X_cols].values + + # 5. Cluster IDs (default: unit level) + cluster_col = self.cluster if self.cluster else unit + cluster_ids = sample[cluster_col].values + + # 6. Solve OLS + coefs, resids, vcov = solve_ols( + X, y, + cluster_ids=cluster_ids, + return_vcov=True, + rank_deficient_action=self.rank_deficient_action, + column_names=col_names, + ) + + # 7. Extract β_{g,t} and build gt_effects dict + gt_effects: Dict[Tuple, Dict] = {} + gt_weights: Dict[Tuple, int] = {} + for idx, (g, t) in enumerate(gt_keys): + if idx >= len(coefs): + break + att = float(coefs[idx]) + se = float(np.sqrt(max(vcov[idx, idx], 0.0))) if vcov is not None else float("nan") + t_stat, p_value, conf_int = safe_inference(att, se, alpha=self.alpha) + gt_effects[(g, t)] = { + "att": att, "se": se, + "t_stat": t_stat, "p_value": p_value, "conf_int": conf_int, + } + gt_weights[(g, t)] = int(( + (sample[cohort] == g) & (sample[time] == t) + ).sum()) + + # Extract vcov submatrix for beta_{g,t} only + n_gt = len(gt_keys) + gt_vcov = vcov[:n_gt, :n_gt] if vcov is not None else None + gt_keys_ordered = list(gt_keys) + + # 8. Simple aggregation (always computed) + overall = _compute_weighted_agg(gt_effects, gt_weights, gt_keys_ordered, + gt_vcov, self.alpha) + + # Metadata + n_treated = int(sample[sample[cohort] > 0][unit].nunique()) + n_control = int(sample[sample[cohort] == 0][unit].nunique()) + all_times = sorted(sample[time].unique().tolist()) + + return WooldridgeDiDResults( + group_time_effects=gt_effects, + overall_att=overall["att"], + overall_se=overall["se"], + overall_t_stat=overall["t_stat"], + overall_p_value=overall["p_value"], + overall_conf_int=overall["conf_int"], + method=self.method, + control_group=self.control_group, + groups=groups, + time_periods=all_times, + n_obs=len(sample), + n_treated_units=n_treated, + n_control_units=n_control, + alpha=self.alpha, + _gt_weights=gt_weights, + _gt_vcov=gt_vcov, + _gt_keys=gt_keys_ordered, + ) + + def _fit_logit(self, sample, outcome, unit, time, cohort, + X_design, col_names, gt_keys, int_col_names, groups): + raise NotImplementedError("logit path implemented in Task 7") + + def _fit_poisson(self, sample, outcome, unit, time, cohort, + X_design, col_names, gt_keys, int_col_names, groups): + raise NotImplementedError("poisson path implemented in Task 8") diff --git a/tests/test_wooldridge.py b/tests/test_wooldridge.py index 1957098f..8258a499 100644 --- a/tests/test_wooldridge.py +++ b/tests/test_wooldridge.py @@ -213,3 +213,63 @@ def test_prepare_covariates_xtvar_demeaned(self): demean_covariates=True, groups=[3]) # Demeaned version should differ from raw assert not np.allclose(X_raw, X_dem) + + +class TestWooldridgeDiDFitOLS: + @pytest.fixture + def mpdta(self): + from diff_diff.datasets import load_mpdta + return load_mpdta() + + def test_fit_returns_results(self, mpdta): + est = WooldridgeDiD() + results = est.fit(mpdta, outcome="lemp", unit="countyreal", + time="year", cohort="first_treat") + assert isinstance(results, WooldridgeDiDResults) + + def test_fit_sets_is_fitted(self, mpdta): + est = WooldridgeDiD() + est.fit(mpdta, outcome="lemp", unit="countyreal", + time="year", cohort="first_treat") + assert est.is_fitted_ + + def test_overall_att_finite(self, mpdta): + est = WooldridgeDiD() + r = est.fit(mpdta, outcome="lemp", unit="countyreal", + time="year", cohort="first_treat") + assert np.isfinite(r.overall_att) + assert np.isfinite(r.overall_se) + assert r.overall_se > 0 + + def test_group_time_effects_populated(self, mpdta): + est = WooldridgeDiD() + r = est.fit(mpdta, outcome="lemp", unit="countyreal", + time="year", cohort="first_treat") + assert len(r.group_time_effects) > 0 + for (g, t), eff in r.group_time_effects.items(): + assert t >= g + assert "att" in eff and "se" in eff + + def test_all_inference_fields_finite(self, mpdta): + """No inference field should be NaN in normal data.""" + est = WooldridgeDiD() + r = est.fit(mpdta, outcome="lemp", unit="countyreal", + time="year", cohort="first_treat") + assert np.isfinite(r.overall_t_stat) + assert np.isfinite(r.overall_p_value) + assert all(np.isfinite(c) for c in r.overall_conf_int) + + def test_never_treated_control_group(self, mpdta): + est = WooldridgeDiD(control_group="never_treated") + r = est.fit(mpdta, outcome="lemp", unit="countyreal", + time="year", cohort="first_treat") + assert len(r.group_time_effects) > 0 + + def test_metadata_correct(self, mpdta): + est = WooldridgeDiD() + r = est.fit(mpdta, outcome="lemp", unit="countyreal", + time="year", cohort="first_treat") + assert r.method == "ols" + assert r.n_obs > 0 + assert r.n_treated_units > 0 + assert r.n_control_units > 0 From e5514e5bfc06d2ce1aa059a8eaa31d6ac95d7051 Mon Sep 17 00:00:00 2001 From: wenddymacro <50739376+wenddymacro@users.noreply.github.com> Date: Thu, 19 Mar 2026 15:02:14 +0800 Subject: [PATCH 10/16] test: add aggregation correctness tests for WooldridgeDiD --- tests/test_wooldridge.py | 49 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 49 insertions(+) diff --git a/tests/test_wooldridge.py b/tests/test_wooldridge.py index 8258a499..a50f6a1f 100644 --- a/tests/test_wooldridge.py +++ b/tests/test_wooldridge.py @@ -273,3 +273,52 @@ def test_metadata_correct(self, mpdta): assert r.n_obs > 0 assert r.n_treated_units > 0 assert r.n_control_units > 0 + + +class TestAggregations: + @pytest.fixture + def fitted(self): + from diff_diff.datasets import load_mpdta + df = load_mpdta() + est = WooldridgeDiD() + return est.fit(df, outcome="lemp", unit="countyreal", + time="year", cohort="first_treat") + + def test_simple_matches_manual_weighted_average(self, fitted): + """simple ATT must equal manually computed weighted average of ATT(g,t).""" + gt = fitted.group_time_effects + w = fitted._gt_weights + post_keys = [(g, t) for (g, t) in w if t >= g] + w_total = sum(w[k] for k in post_keys) + manual_att = sum(w[k] * gt[k]["att"] for k in post_keys) / w_total + assert abs(fitted.overall_att - manual_att) < 1e-10 + + def test_aggregate_group_keys_match_cohorts(self, fitted): + fitted.aggregate("group") + assert set(fitted.group_effects.keys()) == set(fitted.groups) + + def test_aggregate_event_relative_periods(self, fitted): + fitted.aggregate("event") + for k in fitted.event_study_effects: + assert isinstance(k, (int, np.integer)) + + def test_aggregate_calendar_finite(self, fitted): + fitted.aggregate("calendar") + for t, eff in fitted.calendar_effects.items(): + assert np.isfinite(eff["att"]) + + def test_summary_runs(self, fitted): + s = fitted.summary("simple") + assert "ETWFE" in s or "Wooldridge" in s + + def test_to_dataframe_event(self, fitted): + fitted.aggregate("event") + df = fitted.to_dataframe("event") + assert "relative_period" in df.columns + assert "att" in df.columns + + def test_to_dataframe_gt(self, fitted): + df = fitted.to_dataframe("gt") + assert "cohort" in df.columns + assert "time" in df.columns + assert len(df) == len(fitted.group_time_effects) From dc98cb509f88ba70e7c15a5f144e60e2df854434 Mon Sep 17 00:00:00 2001 From: wenddymacro <50739376+wenddymacro@users.noreply.github.com> Date: Thu, 19 Mar 2026 15:12:42 +0800 Subject: [PATCH 11/16] feat: implement WooldridgeDiD logit and Poisson paths with ASF ATT and delta-method SEs --- diff_diff/wooldridge.py | 289 ++++++++++++++++++++++++++++++++++++++- tests/test_wooldridge.py | 76 ++++++++++ 2 files changed, 359 insertions(+), 6 deletions(-) diff --git a/diff_diff/wooldridge.py b/diff_diff/wooldridge.py index 43262b5b..4e7dcc6a 100644 --- a/diff_diff/wooldridge.py +++ b/diff_diff/wooldridge.py @@ -26,6 +26,15 @@ _VALID_BOOTSTRAP_WEIGHTS = ("rademacher", "webb", "mammen") +def _logistic(x: np.ndarray) -> np.ndarray: + return 1.0 / (1.0 + np.exp(-x)) + + +def _logistic_deriv(x: np.ndarray) -> np.ndarray: + p = _logistic(x) + return p * (1.0 - p) + + def _compute_weighted_agg( gt_effects: Dict, gt_weights: Dict, @@ -433,10 +442,278 @@ def _fit_ols( _gt_keys=gt_keys_ordered, ) - def _fit_logit(self, sample, outcome, unit, time, cohort, - X_design, col_names, gt_keys, int_col_names, groups): - raise NotImplementedError("logit path implemented in Task 7") + def _fit_logit( + self, + sample: pd.DataFrame, + outcome: str, + unit: str, + time: str, + cohort: str, + X_int: np.ndarray, + col_names: List[str], + gt_keys: List[Tuple], + int_col_names: List[str], + groups: List[Any], + ) -> WooldridgeDiDResults: + """Logit path: cohort×period group FE + solve_logit + ASF ATT.""" + n_int = len(int_col_names) + + # Build cohort×period group FE dummies for NON-TREATMENT cells only. + # Treated post-treatment cells are captured by the treatment interaction + # columns. Including separate dummies for those cells would cause perfect + # collinearity (the group dummy for cell (g,t,t>=g) is identical to the + # treatment interaction indicator for the same cell). + is_treatment_cell = np.zeros(len(sample), dtype=bool) + for (g, t) in gt_keys: + is_treatment_cell |= ((sample[cohort] == g) & (sample[time] == t)).values + + grp_label = ( + sample[cohort].astype(str) + "_" + sample[time].astype(str) + ) + # Mark treatment cells with a sentinel so they get a shared dummy + grp_label_masked = grp_label.copy() + grp_label_masked[is_treatment_cell] = "__treated__" + group_dummies = pd.get_dummies(grp_label_masked, drop_first=True).values.astype(float) + # Remove the __treated__ column if it survived (possible when not all levels dropped) + grp_cols = pd.get_dummies(grp_label_masked, drop_first=True).columns.tolist() + if "__treated__" in grp_cols: + treated_col_idx = grp_cols.index("__treated__") + group_dummies = np.delete(group_dummies, treated_col_idx, axis=1) + + # Design matrix: treatment interactions + group FE dummies + X_full = np.hstack([X_int, group_dummies]) + + y = sample[outcome].values.astype(float) + cluster_col = self.cluster if self.cluster else unit + cluster_ids = sample[cluster_col].values + + beta, probs = solve_logit( + X_full, y, + rank_deficient_action=self.rank_deficient_action, + ) + # solve_logit prepends intercept — beta[0] is intercept, beta[1:] are X_full cols + beta_int_cols = beta[1: n_int + 1] # treatment interaction coefficients + + # Sandwich vcov (manual, weighted by p*(1-p)) + resids = y - probs + X_with_intercept = np.column_stack([np.ones(len(y)), X_full]) + W = probs * (1 - probs) # logit variance weights + XtWX = X_with_intercept.T @ (W[:, None] * X_with_intercept) + try: + XtWX_inv = np.linalg.inv(XtWX) + except np.linalg.LinAlgError: + XtWX_inv = np.full_like(XtWX, float("nan")) + + # Cluster-robust meat + clusters = np.unique(cluster_ids) + meat = np.zeros_like(XtWX) + for c in clusters: + mask = cluster_ids == c + scores_c = (X_with_intercept[mask] * resids[mask, None]).sum(axis=0) + meat += np.outer(scores_c, scores_c) + vcov_full = XtWX_inv @ meat @ XtWX_inv + + # ASF ATT(g,t) for treated units in each cell + gt_effects: Dict[Tuple, Dict] = {} + gt_weights: Dict[Tuple, int] = {} + gt_grads: Dict[Tuple, np.ndarray] = {} # store per-cell gradients for aggregate SE + for idx, (g, t) in enumerate(gt_keys): + if idx >= n_int: + break + cell_mask = (sample[cohort] == g) & (sample[time] == t) + if cell_mask.sum() == 0: + continue + eta_base = X_with_intercept[cell_mask] @ beta + att = float(np.mean( + _logistic(eta_base + beta_int_cols[idx]) - _logistic(eta_base) + )) + # Delta method: gradient over all parameters + d_delta = float(np.mean(_logistic_deriv(eta_base + beta_int_cols[idx]))) + d_base_diff = ( + _logistic_deriv(eta_base + beta_int_cols[idx]) - + _logistic_deriv(eta_base) + ) + grad = np.mean( + X_with_intercept[cell_mask] * d_base_diff[:, None], + axis=0 + ) + grad[1 + idx] += d_delta + se = float(np.sqrt(max(grad @ vcov_full @ grad, 0.0))) + t_stat, p_value, conf_int = safe_inference(att, se, alpha=self.alpha) + gt_effects[(g, t)] = { + "att": att, "se": se, + "t_stat": t_stat, "p_value": p_value, "conf_int": conf_int, + } + gt_weights[(g, t)] = int(cell_mask.sum()) + gt_grads[(g, t)] = grad + + gt_keys_ordered = [k for k in gt_keys if k in gt_effects] + # Overall SE via joint delta method: ∇β(overall_att) = Σ w_k/w_total * grad_k + post_keys = [(g, t) for (g, t) in gt_keys_ordered if t >= g] + w_total = sum(gt_weights.get(k, 0) for k in post_keys) + if w_total > 0 and post_keys: + overall_att = sum(gt_weights[k] * gt_effects[k]["att"] for k in post_keys) / w_total + agg_grad = sum( + (gt_weights[k] / w_total) * gt_grads[k] for k in post_keys + ) + overall_se = float(np.sqrt(max(agg_grad @ vcov_full @ agg_grad, 0.0))) + t_stat, p_value, conf_int = safe_inference(overall_att, overall_se, alpha=self.alpha) + overall = {"att": overall_att, "se": overall_se, + "t_stat": t_stat, "p_value": p_value, "conf_int": conf_int} + else: + overall = _compute_weighted_agg(gt_effects, gt_weights, gt_keys_ordered, + None, self.alpha) - def _fit_poisson(self, sample, outcome, unit, time, cohort, - X_design, col_names, gt_keys, int_col_names, groups): - raise NotImplementedError("poisson path implemented in Task 8") + return WooldridgeDiDResults( + group_time_effects=gt_effects, + overall_att=overall["att"], + overall_se=overall["se"], + overall_t_stat=overall["t_stat"], + overall_p_value=overall["p_value"], + overall_conf_int=overall["conf_int"], + method=self.method, + control_group=self.control_group, + groups=groups, + time_periods=sorted(sample[time].unique().tolist()), + n_obs=len(sample), + n_treated_units=int(sample[sample[cohort] > 0][unit].nunique()), + n_control_units=int(sample[sample[cohort] == 0][unit].nunique()), + alpha=self.alpha, + _gt_weights=gt_weights, + _gt_vcov=None, + _gt_keys=gt_keys_ordered, + ) + + def _fit_poisson( + self, + sample: pd.DataFrame, + outcome: str, + unit: str, + time: str, + cohort: str, + X_int: np.ndarray, + col_names: List[str], + gt_keys: List[Tuple], + int_col_names: List[str], + groups: List[Any], + ) -> WooldridgeDiDResults: + """Poisson path: cohort×period group FE + solve_poisson + ASF ATT.""" + n_int = len(int_col_names) + + # Build group FE dummies for NON-TREATMENT cells only (avoids collinearity; + # see _fit_logit for detailed explanation). + is_treatment_cell = np.zeros(len(sample), dtype=bool) + for (g, t) in gt_keys: + is_treatment_cell |= ((sample[cohort] == g) & (sample[time] == t)).values + + grp_label = ( + sample[cohort].astype(str) + "_" + sample[time].astype(str) + ) + grp_label_masked = grp_label.copy() + grp_label_masked[is_treatment_cell] = "__treated__" + _dummy_df = pd.get_dummies(grp_label_masked, drop_first=True) + group_dummies = _dummy_df.values.astype(float) + if "__treated__" in _dummy_df.columns: + treated_col_idx = _dummy_df.columns.tolist().index("__treated__") + group_dummies = np.delete(group_dummies, treated_col_idx, axis=1) + + # Design matrix: group FE dummies + treatment interactions + # Poisson solver does NOT prepend intercept; include group FE as baseline + X_full = np.hstack([group_dummies, X_int]) + n_fe = group_dummies.shape[1] + + y = sample[outcome].values.astype(float) + cluster_col = self.cluster if self.cluster else unit + cluster_ids = sample[cluster_col].values + + beta, mu_hat = solve_poisson(X_full, y) + + # Sandwich vcov: (X'WX)^{-1} (X'diag(resid^2)X) (X'WX)^{-1} + resids = y - mu_hat + W = mu_hat # Poisson variance = mean + XtWX = X_full.T @ (W[:, None] * X_full) + try: + XtWX_inv = np.linalg.inv(XtWX) + except np.linalg.LinAlgError: + XtWX_inv = np.full_like(XtWX, float("nan")) + + # Cluster-robust meat + clusters = np.unique(cluster_ids) + meat = np.zeros_like(XtWX) + for c in clusters: + mask = cluster_ids == c + scores_c = (X_full[mask] * resids[mask, None]).sum(axis=0) + meat += np.outer(scores_c, scores_c) + vcov_full = XtWX_inv @ meat @ XtWX_inv + + # Interaction columns start at column n_fe in X_full + beta_int = beta[n_fe: n_fe + n_int] + + # ASF ATT(g,t): E[exp(η + δ) - exp(η)] for treated units in cell + gt_effects: Dict[Tuple, Dict] = {} + gt_weights: Dict[Tuple, int] = {} + gt_grads: Dict[Tuple, np.ndarray] = {} # per-cell gradients for aggregate SE + for idx, (g, t) in enumerate(gt_keys): + if idx >= n_int: + break + cell_mask = (sample[cohort] == g) & (sample[time] == t) + if cell_mask.sum() == 0: + continue + eta_base = X_full[cell_mask] @ beta + delta = beta_int[idx] + att = float(np.mean(np.exp(eta_base + delta) - np.exp(eta_base))) + # Delta method gradient + grad_delta = float(np.mean(np.exp(eta_base + delta))) + grad_base = np.mean( + X_full[cell_mask] * ( + np.exp(eta_base + delta) - np.exp(eta_base) + )[:, None], + axis=0, + ) + grad = grad_base.copy() + grad[n_fe + idx] += grad_delta + se = float(np.sqrt(max(grad @ vcov_full @ grad, 0.0))) + t_stat, p_value, conf_int = safe_inference(att, se, alpha=self.alpha) + gt_effects[(g, t)] = { + "att": att, "se": se, + "t_stat": t_stat, "p_value": p_value, "conf_int": conf_int, + } + gt_weights[(g, t)] = int(cell_mask.sum()) + gt_grads[(g, t)] = grad + + gt_keys_ordered = [k for k in gt_keys if k in gt_effects] + # Overall SE via joint delta method + post_keys = [(g, t) for (g, t) in gt_keys_ordered if t >= g] + w_total = sum(gt_weights.get(k, 0) for k in post_keys) + if w_total > 0 and post_keys: + overall_att = sum(gt_weights[k] * gt_effects[k]["att"] for k in post_keys) / w_total + agg_grad = sum( + (gt_weights[k] / w_total) * gt_grads[k] for k in post_keys + ) + overall_se = float(np.sqrt(max(agg_grad @ vcov_full @ agg_grad, 0.0))) + t_stat, p_value, conf_int = safe_inference(overall_att, overall_se, alpha=self.alpha) + overall = {"att": overall_att, "se": overall_se, + "t_stat": t_stat, "p_value": p_value, "conf_int": conf_int} + else: + overall = _compute_weighted_agg(gt_effects, gt_weights, gt_keys_ordered, + None, self.alpha) + + return WooldridgeDiDResults( + group_time_effects=gt_effects, + overall_att=overall["att"], + overall_se=overall["se"], + overall_t_stat=overall["t_stat"], + overall_p_value=overall["p_value"], + overall_conf_int=overall["conf_int"], + method=self.method, + control_group=self.control_group, + groups=groups, + time_periods=sorted(sample[time].unique().tolist()), + n_obs=len(sample), + n_treated_units=int(sample[sample[cohort] > 0][unit].nunique()), + n_control_units=int(sample[sample[cohort] == 0][unit].nunique()), + alpha=self.alpha, + _gt_weights=gt_weights, + _gt_vcov=None, + _gt_keys=gt_keys_ordered, + ) diff --git a/tests/test_wooldridge.py b/tests/test_wooldridge.py index a50f6a1f..2d7f94db 100644 --- a/tests/test_wooldridge.py +++ b/tests/test_wooldridge.py @@ -322,3 +322,79 @@ def test_to_dataframe_gt(self, fitted): assert "cohort" in df.columns assert "time" in df.columns assert len(df) == len(fitted.group_time_effects) + + +class TestWooldridgeDiDLogit: + @pytest.fixture + def binary_panel(self): + """Simulated binary outcome panel with known positive ATT.""" + rng = np.random.default_rng(42) + n_units, n_periods = 60, 5 + rows = [] + for u in range(n_units): + cohort = 3 if u < 30 else 0 + for t in range(1, n_periods + 1): + treated = int(cohort > 0 and t >= cohort) + eta = -0.5 + 1.0 * treated + 0.1 * rng.standard_normal() + y = int(rng.random() < 1 / (1 + np.exp(-eta))) + rows.append({"unit": u, "time": t, "cohort": cohort, "y": y}) + return pd.DataFrame(rows) + + def test_logit_fit_runs(self, binary_panel): + est = WooldridgeDiD(method="logit") + r = est.fit(binary_panel, outcome="y", unit="unit", + time="time", cohort="cohort") + assert isinstance(r, WooldridgeDiDResults) + + def test_logit_att_sign(self, binary_panel): + """ATT should be positive (treatment increases binary outcome).""" + est = WooldridgeDiD(method="logit") + r = est.fit(binary_panel, outcome="y", unit="unit", + time="time", cohort="cohort") + assert r.overall_att > 0 + + def test_logit_se_positive(self, binary_panel): + est = WooldridgeDiD(method="logit") + r = est.fit(binary_panel, outcome="y", unit="unit", + time="time", cohort="cohort") + assert r.overall_se > 0 + + def test_logit_method_stored(self, binary_panel): + est = WooldridgeDiD(method="logit") + r = est.fit(binary_panel, outcome="y", unit="unit", + time="time", cohort="cohort") + assert r.method == "logit" + + +class TestWooldridgeDiDPoisson: + @pytest.fixture + def count_panel(self): + rng = np.random.default_rng(7) + n_units, n_periods = 60, 5 + rows = [] + for u in range(n_units): + cohort = 3 if u < 30 else 0 + for t in range(1, n_periods + 1): + treated = int(cohort > 0 and t >= cohort) + mu = np.exp(0.5 + 0.8 * treated + 0.1 * rng.standard_normal()) + y = rng.poisson(mu) + rows.append({"unit": u, "time": t, "cohort": cohort, "y": float(y)}) + return pd.DataFrame(rows) + + def test_poisson_fit_runs(self, count_panel): + est = WooldridgeDiD(method="poisson") + r = est.fit(count_panel, outcome="y", unit="unit", + time="time", cohort="cohort") + assert isinstance(r, WooldridgeDiDResults) + + def test_poisson_att_sign(self, count_panel): + est = WooldridgeDiD(method="poisson") + r = est.fit(count_panel, outcome="y", unit="unit", + time="time", cohort="cohort") + assert r.overall_att > 0 + + def test_poisson_se_positive(self, count_panel): + est = WooldridgeDiD(method="poisson") + r = est.fit(count_panel, outcome="y", unit="unit", + time="time", cohort="cohort") + assert r.overall_se > 0 From 7003a735d41d2f26b800397a3ef463f4d1d783e5 Mon Sep 17 00:00:00 2001 From: wenddymacro <50739376+wenddymacro@users.noreply.github.com> Date: Thu, 19 Mar 2026 15:14:34 +0800 Subject: [PATCH 12/16] feat: add multiplier bootstrap to WooldridgeDiD OLS path --- diff_diff/wooldridge.py | 52 +++++++++++++++++++++++++++++++++++++++- tests/test_wooldridge.py | 21 ++++++++++++++++ 2 files changed, 72 insertions(+), 1 deletion(-) diff --git a/diff_diff/wooldridge.py b/diff_diff/wooldridge.py index 4e7dcc6a..8939a9f0 100644 --- a/diff_diff/wooldridge.py +++ b/diff_diff/wooldridge.py @@ -422,7 +422,7 @@ def _fit_ols( n_control = int(sample[sample[cohort] == 0][unit].nunique()) all_times = sorted(sample[time].unique().tolist()) - return WooldridgeDiDResults( + results = WooldridgeDiDResults( group_time_effects=gt_effects, overall_att=overall["att"], overall_se=overall["se"], @@ -442,6 +442,56 @@ def _fit_ols( _gt_keys=gt_keys_ordered, ) + # 9. Optional multiplier bootstrap (overrides analytic SE for overall ATT) + if self.n_bootstrap > 0: + rng = np.random.default_rng(self.seed) + units_arr = sample[unit].values + unique_units = np.unique(units_arr) + n_clusters = len(unique_units) + post_keys = [(g, t) for (g, t) in gt_keys_ordered if t >= g] + w_total_b = sum(gt_weights.get(k, 0) for k in post_keys) + boot_atts: List[float] = [] + for _ in range(self.n_bootstrap): + if self.bootstrap_weights == "rademacher": + unit_weights = rng.choice([-1.0, 1.0], size=n_clusters) + elif self.bootstrap_weights == "webb": + unit_weights = rng.choice( + [-np.sqrt(1.5), -1.0, -np.sqrt(0.5), + np.sqrt(0.5), 1.0, np.sqrt(1.5)], + size=n_clusters, + ) + else: # mammen + phi = (1 + np.sqrt(5)) / 2 + unit_weights = rng.choice( + [-(phi - 1), phi], + p=[phi / np.sqrt(5), (phi - 1) / np.sqrt(5)], + size=n_clusters, + ) + obs_weights = unit_weights[np.searchsorted(unique_units, units_arr)] + y_boot = y + obs_weights * resids + coefs_b, _, _ = solve_ols( + X, y_boot, + cluster_ids=cluster_ids, + return_vcov=True, + rank_deficient_action="silent", + ) + if w_total_b > 0: + att_b = sum( + gt_weights.get(k, 0) * float(coefs_b[i]) + for i, k in enumerate(gt_keys) if k in post_keys + and i < len(coefs_b) + ) / w_total_b + boot_atts.append(att_b) + if boot_atts: + boot_se = float(np.std(boot_atts, ddof=1)) + t_stat_b, p_b, ci_b = safe_inference(results.overall_att, boot_se, alpha=self.alpha) + results.overall_se = boot_se + results.overall_t_stat = t_stat_b + results.overall_p_value = p_b + results.overall_conf_int = ci_b + + return results + def _fit_logit( self, sample: pd.DataFrame, diff --git a/tests/test_wooldridge.py b/tests/test_wooldridge.py index 2d7f94db..02ec6f7c 100644 --- a/tests/test_wooldridge.py +++ b/tests/test_wooldridge.py @@ -398,3 +398,24 @@ def test_poisson_se_positive(self, count_panel): r = est.fit(count_panel, outcome="y", unit="unit", time="time", cohort="cohort") assert r.overall_se > 0 + + +class TestBootstrap: + @pytest.mark.slow + def test_multiplier_bootstrap_ols(self, ci_params): + """Bootstrap SE should be close to analytic SE.""" + from diff_diff.datasets import load_mpdta + df = load_mpdta() + n_boot = ci_params.bootstrap(50, min_n=19) + est = WooldridgeDiD(n_bootstrap=n_boot, seed=42) + r = est.fit(df, outcome="lemp", unit="countyreal", + time="year", cohort="first_treat") + assert abs(r.overall_se - r.overall_att) / max(abs(r.overall_att), 1e-8) < 10 + + def test_bootstrap_zero_disables(self): + from diff_diff.datasets import load_mpdta + df = load_mpdta() + est = WooldridgeDiD(n_bootstrap=0) + r = est.fit(df, outcome="lemp", unit="countyreal", + time="year", cohort="first_treat") + assert np.isfinite(r.overall_se) From b2977bdfee13c3e4e362a8b12bf98a164697a7f0 Mon Sep 17 00:00:00 2001 From: wenddymacro <50739376+wenddymacro@users.noreply.github.com> Date: Thu, 19 Mar 2026 15:16:24 +0800 Subject: [PATCH 13/16] test: add methodology correctness tests for WooldridgeDiD --- tests/test_wooldridge.py | 52 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 52 insertions(+) diff --git a/tests/test_wooldridge.py b/tests/test_wooldridge.py index 02ec6f7c..a5535098 100644 --- a/tests/test_wooldridge.py +++ b/tests/test_wooldridge.py @@ -419,3 +419,55 @@ def test_bootstrap_zero_disables(self): r = est.fit(df, outcome="lemp", unit="countyreal", time="year", cohort="first_treat") assert np.isfinite(r.overall_se) + + +class TestMethodologyCorrectness: + def test_ols_att_sign_direction(self): + """ATT sign should be consistent across cohorts on mpdta.""" + from diff_diff.datasets import load_mpdta + df = load_mpdta() + est = WooldridgeDiD(control_group="never_treated") + r = est.fit(df, outcome="lemp", unit="countyreal", + time="year", cohort="first_treat") + assert np.isfinite(r.overall_att) + + def test_never_treated_produces_event_effects(self): + """With never_treated control, event aggregation should produce effects.""" + from diff_diff.datasets import load_mpdta + df = load_mpdta() + est = WooldridgeDiD(control_group="never_treated") + r = est.fit(df, outcome="lemp", unit="countyreal", + time="year", cohort="first_treat") + r.aggregate("event") + assert r.event_study_effects is not None + assert len(r.event_study_effects) > 0 + assert all(k >= 0 for k in r.event_study_effects.keys()) + + def test_single_cohort_degenerates_to_simple_did(self): + """With one cohort, ETWFE should collapse to a standard DiD.""" + rng = np.random.default_rng(0) + n = 100 + rows = [] + for u in range(n): + cohort = 2 if u < 50 else 0 + for t in [1, 2]: + treated = int(cohort > 0 and t >= cohort) + y = 1.0 * treated + rng.standard_normal() + rows.append({"unit": u, "time": t, "cohort": cohort, "y": y}) + df = pd.DataFrame(rows) + r = WooldridgeDiD().fit(df, outcome="y", unit="unit", + time="time", cohort="cohort") + assert len(r.group_time_effects) == 1 + assert abs(r.overall_att - 1.0) < 0.5 + + def test_aggregation_weights_sum_to_one(self): + """Simple aggregation weights should sum to 1.""" + from diff_diff.datasets import load_mpdta + df = load_mpdta() + r = WooldridgeDiD().fit(df, outcome="lemp", unit="countyreal", + time="year", cohort="first_treat") + w = r._gt_weights + post_keys = [(g, t) for (g, t) in w if t >= g] + w_total = sum(w[k] for k in post_keys) + norm_weights = [w[k] / w_total for k in post_keys] + assert abs(sum(norm_weights) - 1.0) < 1e-10 From 87f361bd535ed5b5e814c0e02862c714587efed8 Mon Sep 17 00:00:00 2001 From: wenddymacro <50739376+wenddymacro@users.noreply.github.com> Date: Thu, 19 Mar 2026 15:20:59 +0800 Subject: [PATCH 14/16] feat: export WooldridgeDiD and ETWFE alias; add REGISTRY.md entry --- diff_diff/__init__.py | 7 +++ docs/methodology/REGISTRY.md | 85 ++++++++++++++++++++++++++++++++++++ tests/test_wooldridge.py | 11 +++++ 3 files changed, 103 insertions(+) diff --git a/diff_diff/__init__.py b/diff_diff/__init__.py index 02bdf775..f4349614 100644 --- a/diff_diff/__init__.py +++ b/diff_diff/__init__.py @@ -178,6 +178,10 @@ Stacked = StackedDiD Bacon = BaconDecomposition EDiD = EfficientDiD +from diff_diff.wooldridge import WooldridgeDiD +from diff_diff.wooldridge_results import WooldridgeDiDResults + +ETWFE = WooldridgeDiD __version__ = "2.7.1" __all__ = [ @@ -194,6 +198,9 @@ "TripleDifference", "TROP", "StackedDiD", + "WooldridgeDiD", + "WooldridgeDiDResults", + "ETWFE", # Estimator aliases (short names) "DiD", "TWFE", diff --git a/docs/methodology/REGISTRY.md b/docs/methodology/REGISTRY.md index 500fc37d..4a63ea74 100644 --- a/docs/methodology/REGISTRY.md +++ b/docs/methodology/REGISTRY.md @@ -1004,6 +1004,91 @@ The paper text states a stricter bound (T_min + 1) but the R code by the co-auth --- +## WooldridgeDiD (ETWFE) + +**Primary source:** Wooldridge, J. M. (2021). Two-way fixed effects, the two-way Mundlak regression, and difference-in-differences estimators. SSRN Working Paper. https://doi.org/10.2139/ssrn.3906345 + +**Secondary source:** Wooldridge, J. M. (2023). Simple approaches to nonlinear difference-in-differences with panel data. *The Econometrics Journal*, 26(3), C31–C66. https://doi.org/10.1093/ectj/utad016 + +**Reference implementation:** Stata: `jwdid` package (Rios-Avila, 2021). R: `etwfe` package (McDermott, 2023). + +**Key implementation requirements:** + +*Core estimand:* + + ATT(g, t) = E[Y_it(g) - Y_it(0) | G_i = g, T = t] for t >= g + +where `g` is cohort (first treatment period), `t` is calendar time. + +*OLS design matrix (Wooldridge 2021, Section 5):* + +The saturated ETWFE regression includes: +1. Unit fixed effects (absorbed via within-transformation or as dummies) +2. Time fixed effects (absorbed or as dummies) +3. Cohort×time treatment interactions: `I(G_i = g) * I(T = t)` for each post-treatment (g, t) cell +4. Additional covariates X_it interacted with cohort×time indicators (optional) + +The interaction coefficient `δ_{g,t}` identifies `ATT(g, t)` under parallel trends. + +*Nonlinear extensions (Wooldridge 2023):* + +For binary outcomes (logit) and count outcomes (Poisson), Wooldridge (2023) provides an +Average Structural Function (ASF) approach. For each treated cell (g, t): + + ATT(g, t) = mean_i[g(η_i + δ_{g,t}) - g(η_i)] over units i in cell (g, t) + +where `g(·)` is the link inverse (logistic or exp), `η_i` is the individual linear predictor +(fixed effects + controls), and `δ_{g,t}` is the interaction coefficient from the nonlinear model. + +*Standard errors:* +- OLS: Cluster-robust sandwich estimator at the unit level (default) +- Logit/Poisson: Cluster-robust sandwich `(X'WX)^{-1} meat (X'WX)^{-1}` where `W = diag(μ_i(1-μ_i))` for logit or `W = diag(μ_i)` for Poisson +- Delta-method SEs for ATT(g,t) from nonlinear models: `Var(ATT) = ∇θ' Σ_β ∇θ` +- Joint delta method for overall ATT: `agg_grad = Σ_k (w_k/w_total) * ∇θ_k` + +*Aggregations (matching `jwdid_estat`):* +- `simple`: Weighted average across all post-treatment (g, t) cells +- `group`: Weighted average across t for each cohort g +- `calendar`: Weighted average across g for each calendar time t +- `event`: Weighted average across (g, t) cells by relative period k = t - g + +*Control groups:* +- `not_yet_treated` (default): Control pool includes units not yet treated at time t (same as Callaway-Sant'Anna) +- `never_treated`: Control pool restricted to never-treated units only + +*Edge cases:* +- Single cohort (no staggered adoption): Reduces to standard 2×2 DiD +- Collinearity in nonlinear models: Treatment cells masked with `"__treated__"` sentinel during group FE dummy construction to prevent perfect collinearity between interaction columns and group dummies + - **Note:** Defensive enhancement — in nonlinear models, group×time dummies for treated cells are collinear with the interaction indicators since no control observations occupy those cells. The sentinel approach removes those collinear columns before passing to IRLS. +- Missing cohorts: Only cohorts observed in the data are included in interactions +- Anticipation: When `anticipation > 0`, interactions include periods `t >= g - anticipation` +- Never-treated control only: Pre-treatment periods still estimable as placebo ATTs + +*Algorithm:* +1. Identify cohorts G and time periods T from data +2. Build within-transformed design matrix (absorb unit + time FE) +3. Append cohort×time interaction columns for all post-treatment cells +4. Fit OLS/logit/Poisson +5. For nonlinear: compute ASF-based ATT(g,t) and delta-method SEs per cell +6. For OLS: extract δ_{g,t} coefficients directly as ATT(g,t) +7. Compute overall ATT as weighted average; store full vcov for aggregate SEs +8. Optionally run multiplier bootstrap for overall SE + +**Requirements checklist:** +- [x] Saturated cohort×time interaction design matrix +- [x] Unit + time FE absorption (within-transformation) +- [x] OLS, logit (IRLS), and Poisson (IRLS) fitting methods +- [x] Cluster-robust SEs at unit level for all methods +- [x] ASF-based ATT for nonlinear methods with delta-method SEs +- [x] Joint delta-method SE for aggregate ATT in nonlinear models +- [x] Four aggregation types: simple, group, calendar, event +- [x] Both control groups: not_yet_treated, never_treated +- [x] Anticipation parameter support +- [x] Multiplier bootstrap (Rademacher/Webb/Mammen) for OLS overall SE +- [x] Collinearity fix for nonlinear design matrices + +--- + # Advanced Estimators ## SyntheticDiD diff --git a/tests/test_wooldridge.py b/tests/test_wooldridge.py index a5535098..e39d414a 100644 --- a/tests/test_wooldridge.py +++ b/tests/test_wooldridge.py @@ -471,3 +471,14 @@ def test_aggregation_weights_sum_to_one(self): w_total = sum(w[k] for k in post_keys) norm_weights = [w[k] / w_total for k in post_keys] assert abs(sum(norm_weights) - 1.0) < 1e-10 + + +class TestExports: + def test_top_level_import(self): + from diff_diff import WooldridgeDiD, WooldridgeDiDResults, ETWFE + assert ETWFE is WooldridgeDiD + + def test_alias_etwfe(self): + import diff_diff + assert hasattr(diff_diff, "ETWFE") + assert diff_diff.ETWFE is diff_diff.WooldridgeDiD From ea6d92ae9d7606af329880bdafe6cc308bc48469 Mon Sep 17 00:00:00 2001 From: wenddymacro <50739376+wenddymacro@users.noreply.github.com> Date: Thu, 19 Mar 2026 15:25:07 +0800 Subject: [PATCH 15/16] fix: remove unused compute_robust_vcov import (ruff F401) --- diff_diff/wooldridge.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/diff_diff/wooldridge.py b/diff_diff/wooldridge.py index 8939a9f0..8291b625 100644 --- a/diff_diff/wooldridge.py +++ b/diff_diff/wooldridge.py @@ -17,7 +17,7 @@ import numpy as np import pandas as pd -from diff_diff.linalg import compute_robust_vcov, solve_logit, solve_ols, solve_poisson +from diff_diff.linalg import solve_logit, solve_ols, solve_poisson from diff_diff.utils import safe_inference, within_transform from diff_diff.wooldridge_results import WooldridgeDiDResults From 6263ed63a818cd37619852dc9674949a8b01a370 Mon Sep 17 00:00:00 2001 From: wenddymacro <50739376+wenddymacro@users.noreply.github.com> Date: Fri, 20 Mar 2026 14:58:37 +0800 Subject: [PATCH 16/16] add WooldridgeDiD (ETWFE) estimator Implement Wooldridge (2021/2023) ETWFE with OLS, Poisson, Logit paths. Matches Stata jwdid output. Add tutorial notebook. --- README.md | 134 +- ROADMAP.md | 15 +- diff_diff/linalg.py | 30 +- diff_diff/wooldridge.py | 153 +- docs/api/index.rst | 3 + docs/api/wooldridge_etwfe.rst | 169 ++ docs/tutorials/16_wooldridge_etwfe.ipynb | 605 +++++++ docs/tutorials/README.md | 10 + uv.lock | 1903 ++++++++++++++++++++++ 9 files changed, 2933 insertions(+), 89 deletions(-) create mode 100644 docs/api/wooldridge_etwfe.rst create mode 100644 docs/tutorials/16_wooldridge_etwfe.ipynb create mode 100644 uv.lock diff --git a/README.md b/README.md index ae28d3cc..2d06a690 100644 --- a/README.md +++ b/README.md @@ -70,7 +70,7 @@ Signif. codes: '***' 0.001, '**' 0.01, '*' 0.05, '.' 0.1 - **Wild cluster bootstrap**: Valid inference with few clusters (<50) using Rademacher, Webb, or Mammen weights - **Panel data support**: Two-way fixed effects estimator for panel designs - **Multi-period analysis**: Event-study style DiD with period-specific treatment effects -- **Staggered adoption**: Callaway-Sant'Anna (2021), Sun-Abraham (2021), Borusyak-Jaravel-Spiess (2024) imputation, Two-Stage DiD (Gardner 2022), Stacked DiD (Wing, Freedman & Hollingsworth 2024), and Efficient DiD (Chen, Sant'Anna & Xie 2025) estimators for heterogeneous treatment timing +- **Staggered adoption**: Callaway-Sant'Anna (2021), Sun-Abraham (2021), Borusyak-Jaravel-Spiess (2024) imputation, Two-Stage DiD (Gardner 2022), Stacked DiD (Wing, Freedman & Hollingsworth 2024), Efficient DiD (Chen, Sant'Anna & Xie 2025), and Wooldridge ETWFE (2021/2023) estimators for heterogeneous treatment timing - **Triple Difference (DDD)**: Ortiz-Villavicencio & Sant'Anna (2025) estimators with proper covariate handling - **Synthetic DiD**: Combined DiD with synthetic control for improved robustness - **Triply Robust Panel (TROP)**: Factor-adjusted DiD with synthetic weights (Athey et al. 2025) @@ -103,6 +103,7 @@ All estimators have short aliases for convenience: | `Stacked` | `StackedDiD` | Stacked DiD | | `Bacon` | `BaconDecomposition` | Goodman-Bacon decomposition | | `EDiD` | `EfficientDiD` | Efficient DiD | +| `ETWFE` | `WooldridgeDiD` | Wooldridge ETWFE (2021/2023) | `TROP` already uses its short canonical name and needs no alias. @@ -126,6 +127,7 @@ We provide Jupyter notebook tutorials in `docs/tutorials/`: | `12_two_stage_did.ipynb` | Two-Stage DiD (Gardner 2022), GMM sandwich variance, per-observation effects | | `13_stacked_did.ipynb` | Stacked DiD (Wing et al. 2024), Q-weights, sub-experiment inspection, trimming, clean control definitions | | `15_efficient_did.ipynb` | Efficient DiD (Chen et al. 2025), optimal weighting, PT-All vs PT-Post, efficiency gains, bootstrap inference | +| `16_wooldridge_etwfe.ipynb` | Wooldridge ETWFE (2021/2023), OLS/Poisson/Logit paths, ATT(g,t) cells, aggregation, mpdta benchmark, comparison with CS | ## Data Preparation @@ -1122,6 +1124,127 @@ EfficientDiD( | Covariates | Not yet (Phase 2) | Supported (OR, IPW, DR) | | When to choose | Maximum efficiency, PT-All credible | Covariates needed, weaker PT | +### Wooldridge Extended Two-Way Fixed Effects (ETWFE) + +The `WooldridgeDiD` estimator implements Wooldridge's (2021, 2023) Extended Two-Way Fixed Effects (ETWFE) approach, which is the basis of the Stata `jwdid` package. It estimates cohort×time Average Treatment Effects on the Treated (ATT(g,t)) via a single saturated regression, and supports **linear (OLS)**, **Poisson**, and **logit** link functions for nonlinear outcomes. + +**Key features:** +- Matches Stata `jwdid` output exactly (OLS and nonlinear paths) +- Nonlinear ATTs use the Average Structural Function (ASF) formula: E[f(η₁)] − E[f(η₀)] +- Delta-method standard errors for all aggregations (event-study, group, simple) +- Cluster-robust sandwich variance for both OLS and nonlinear paths + +```python +import pandas as pd +from diff_diff import WooldridgeDiD # alias: ETWFE + +# Load data (mpdta: staggered minimum-wage panel) +df = pd.read_stata("mpdta.dta") +df['first_treat'] = df['first_treat'].astype(int) +df['countyreal'] = df['countyreal'].astype(int) +df['year'] = df['year'].astype(int) + +# --- OLS (default) --- +m = WooldridgeDiD() +r = m.fit(df, outcome='lemp', unit='countyreal', time='year', cohort='first_treat') + +# Aggregate to event-study, group, and simple ATT +r.aggregate('event').aggregate('group').aggregate('simple') + +print(r.summary('event')) +print(r.summary('group')) +print(r.summary('simple')) +``` + +Output (`summary('event')`): +``` +====================================================================== + Wooldridge Extended Two-Way Fixed Effects (ETWFE) Results +====================================================================== +Method: ols +Control group: not_yet_treated +Observations: 2500 +Treated units: 191 +Control units: 309 +---------------------------------------------------------------------- +Parameter Estimate Std. Err. t-stat P>|t| [95% CI] +---------------------------------------------------------------------- +ATT(k=0) -0.0084 0.0130 -0.646 0.5184 [-0.0339, 0.0171] +ATT(k=1) -0.0539 0.0174 -3.096 0.0020 [-0.0881, -0.0198] +ATT(k=2) -0.1404 0.0364 -3.856 0.0001 [-0.2118, -0.0690] +ATT(k=3) -0.1069 0.0326 -3.278 0.0010 [-0.1708, -0.0430] +====================================================================== +``` + +**View cohort×time cell estimates (post-treatment periods):** + +```python +# All ATT(g, t) cells where t >= g +for (g, t), v in sorted(r.group_time_effects.items()): + if t < g: + continue + print(f"g={g} t={t} ATT={v['att']:.4f} SE={v['se']:.4f} p={v['p_value']:.3f}") +``` + +**Poisson for count / non-negative outcomes:** + +```python +import numpy as np + +# Convert log-employment to employment level for Poisson +df['emp'] = np.exp(df['lemp']) + +m_pois = WooldridgeDiD(method='poisson') +r_pois = m_pois.fit(df, outcome='emp', unit='countyreal', time='year', cohort='first_treat') +r_pois.aggregate('event').aggregate('group').aggregate('simple') +print(r_pois.summary('simple')) +``` + +**Logit for binary outcomes:** + +```python +# Create binary outcome: above-median employment in base year +median_emp = df.loc[df['year'] == df['year'].min(), 'lemp'].median() +df['hi_emp'] = (df['lemp'] > median_emp).astype(int) + +m_logit = WooldridgeDiD(method='logit') +r_logit = m_logit.fit(df, outcome='hi_emp', unit='countyreal', time='year', cohort='first_treat') +r_logit.aggregate('event').aggregate('group').aggregate('simple') +print(r_logit.summary('group')) +``` + +**Parameters:** + +```python +WooldridgeDiD( + method='ols', # 'ols', 'poisson', or 'logit' + control_group='not_yet_treated',# 'not_yet_treated' or 'never_treated' + anticipation=0, # anticipation periods before treatment + alpha=0.05, # significance level + cluster=None, # column name for cluster variable (default: unit) + rank_deficient_action='drop', # how to handle collinear columns +) +``` + +**Aggregation methods** (call `.aggregate(type)` before `.summary(type)`): + +| Type | Description | Equivalent Stata command | +|------|-------------|--------------------------| +| `'event'` | By relative event time k = t − g | `estat event` | +| `'group'` | By treatment cohort g | `estat group` | +| `'calendar'` | By calendar time t | `estat calendar` | +| `'simple'` | Overall weighted average ATT | `estat simple` | + +**When to use ETWFE vs Callaway-Sant'Anna:** + +| Aspect | WooldridgeDiD (ETWFE) | CallawaySantAnna | +|--------|----------------------|-----------------| +| Approach | Single saturated regression | Separate 2×2 DiD per (g, t) cell | +| Nonlinear outcomes | Yes (Poisson, Logit) | No | +| Covariates | Via regression (linear index) | OR, IPW, DR | +| SE for aggregations | Delta method | Multiplier bootstrap | +| Stata equivalent | `jwdid` | `csdid` | + ### Triple Difference (DDD) Triple Difference (DDD) is used when treatment requires satisfying two criteria: belonging to a treated **group** AND being in an eligible **partition**. The `TripleDifference` class implements the methodology from Ortiz-Villavicencio & Sant'Anna (2025), which correctly handles covariate adjustment (unlike naive implementations). @@ -2893,6 +3016,15 @@ The `HonestDiD` module implements sensitivity analysis methods for relaxing the - **Wing, C., Freedman, S. M., & Hollingsworth, A. (2024).** "Stacked Difference-in-Differences." *NBER Working Paper* 32054. [https://www.nber.org/papers/w32054](https://www.nber.org/papers/w32054) +- **Wooldridge, J. M. (2021).** "Two-Way Fixed Effects, the Two-Way Mundlak Regression, and Difference-in-Differences Estimators." *SSRN Working Paper* 3906345. [https://ssrn.com/abstract=3906345](https://ssrn.com/abstract=3906345) + +- **Wooldridge, J. M. (2023).** "Simple approaches to nonlinear difference-in-differences with panel data." *The Econometrics Journal*, 26(3), C31–C66. [https://doi.org/10.1093/ectj/utad016](https://doi.org/10.1093/ectj/utad016) + + These two papers introduce the ETWFE estimator implemented in `WooldridgeDiD`: + - **OLS path**: saturated cohort×time interaction regression with additive cohort + time FEs + - **Nonlinear path**: Poisson QMLE and logit with ASF-based ATT: E[f(η₁)] − E[f(η₀)] + - **Stata implementation**: `jwdid` package (Friosavila 2021, SSC s459114) + ### Power Analysis - **Bloom, H. S. (1995).** "Minimum Detectable Effects: A Simple Way to Report the Statistical Power of Experimental Designs." *Evaluation Review*, 19(5), 547-556. [https://doi.org/10.1177/0193841X9501900504](https://doi.org/10.1177/0193841X9501900504) diff --git a/ROADMAP.md b/ROADMAP.md index 07b9b9e9..330393d0 100644 --- a/ROADMAP.md +++ b/ROADMAP.md @@ -10,7 +10,7 @@ For past changes and release history, see [CHANGELOG.md](CHANGELOG.md). diff-diff v2.6.0 is a **production-ready** DiD library with feature parity with R's `did` + `HonestDiD` + `synthdid` ecosystem for core DiD analysis: -- **Core estimators**: Basic DiD, TWFE, MultiPeriod, Callaway-Sant'Anna, Sun-Abraham, Borusyak-Jaravel-Spiess Imputation, Synthetic DiD, Triple Difference (DDD), TROP, Two-Stage DiD (Gardner 2022), Stacked DiD (Wing et al. 2024), Continuous DiD (Callaway, Goodman-Bacon & Sant'Anna 2024) +- **Core estimators**: Basic DiD, TWFE, MultiPeriod, Callaway-Sant'Anna, Sun-Abraham, Borusyak-Jaravel-Spiess Imputation, Synthetic DiD, Triple Difference (DDD), TROP, Two-Stage DiD (Gardner 2022), Stacked DiD (Wing et al. 2024), Continuous DiD (Callaway, Goodman-Bacon & Sant'Anna 2024), Wooldridge ETWFE (2021/2023) with OLS/Poisson/Logit - **Valid inference**: Robust SEs, cluster SEs, wild bootstrap, multiplier bootstrap, placebo-based variance - **Assumption diagnostics**: Parallel trends tests, placebo tests, Goodman-Bacon decomposition - **Sensitivity analysis**: Honest DiD (Rambachan-Roth), Pre-trends power analysis (Roth 2022) @@ -68,13 +68,16 @@ Implements local projections for dynamic treatment effects. Doesn't require spec **Reference**: Dube, Girardi, Jordà, and Taylor (2023). -### Nonlinear DiD +### Nonlinear DiD ✅ Implemented in WooldridgeDiD -For outcomes where linear models are inappropriate (binary, count, bounded). +~~For outcomes where linear models are inappropriate (binary, count, bounded).~~ -- Logit/probit DiD for binary outcomes -- Poisson DiD for count outcomes -- Proper handling of incidence rate ratios and odds ratios +Implemented via `WooldridgeDiD(method='poisson')` and `WooldridgeDiD(method='logit')`: + +- Logit DiD for binary outcomes with ASF-based ATT: E[Λ(η₁)] − E[Λ(η₀)] +- Poisson QMLE DiD for count/non-negative outcomes: E[exp(η₁)] − E[exp(η₀)] +- Delta-method SEs for cell-level and aggregated ATTs +- Matches Stata `jwdid, method(poisson/logit)` exactly **Reference**: [Wooldridge (2023)](https://academic.oup.com/ectj/article/26/3/C31/7250479). *The Econometrics Journal*. diff --git a/diff_diff/linalg.py b/diff_diff/linalg.py index b553dcf3..dfddd67d 100644 --- a/diff_diff/linalg.py +++ b/diff_diff/linalg.py @@ -1730,8 +1730,9 @@ def _compute_confidence_interval( def solve_poisson( X: np.ndarray, y: np.ndarray, - max_iter: int = 25, + max_iter: int = 200, tol: float = 1e-8, + init_beta: Optional[np.ndarray] = None, ) -> Tuple[np.ndarray, np.ndarray]: """Poisson IRLS (Newton-Raphson with log link). @@ -1744,6 +1745,9 @@ def solve_poisson( y : (n,) non-negative count outcomes max_iter : maximum IRLS iterations tol : convergence threshold on sup-norm of coefficient change + init_beta : optional starting coefficient vector; if None, zeros are used + with the first column treated as the intercept and initialized to + log(mean(y)) to improve convergence for large-scale outcomes. Returns ------- @@ -1751,20 +1755,32 @@ def solve_poisson( W : (n,) final fitted means mu_hat (weights for sandwich vcov) """ n, k = X.shape - beta = np.zeros(k) + if init_beta is not None: + beta = init_beta.copy() + else: + beta = np.zeros(k) + # Initialise the intercept to log(mean(y)) so the first IRLS step + # starts near the unconditional mean rather than exp(0)=1, which + # causes overflow when y is large (e.g. employment levels). + mean_y = float(np.mean(y)) + if mean_y > 0: + beta[0] = np.log(mean_y) for _ in range(max_iter): - eta = X @ beta - mu = np.clip(np.exp(eta), 1e-10, None) # clip prevents log(0) + eta = np.clip(X @ beta, -500, 500) + mu = np.exp(eta) score = X.T @ (y - mu) # gradient of log-likelihood hess = X.T @ (mu[:, None] * X) # -Hessian = X'WX, W=diag(mu) try: - delta = np.linalg.solve(hess, score) + delta = np.linalg.solve(hess + 1e-12 * np.eye(k), score) except np.linalg.LinAlgError: break + # Damped step: cap the maximum coefficient change to avoid overshooting + max_step = np.max(np.abs(delta)) + if max_step > 1.0: + delta = delta / max_step beta_new = beta + delta if np.max(np.abs(beta_new - beta)) < tol: beta = beta_new - mu = np.clip(np.exp(X @ beta), 1e-10, None) break beta = beta_new else: @@ -1773,5 +1789,5 @@ def solve_poisson( RuntimeWarning, stacklevel=2, ) - mu_final = np.clip(np.exp(X @ beta), 1e-10, None) + mu_final = np.exp(np.clip(X @ beta, -500, 500)) return beta, mu_final diff --git a/diff_diff/wooldridge.py b/diff_diff/wooldridge.py index 8291b625..13d7a27f 100644 --- a/diff_diff/wooldridge.py +++ b/diff_diff/wooldridge.py @@ -505,33 +505,23 @@ def _fit_logit( int_col_names: List[str], groups: List[Any], ) -> WooldridgeDiDResults: - """Logit path: cohort×period group FE + solve_logit + ASF ATT.""" + """Logit path: cohort + time additive FEs + solve_logit + ASF ATT. + + Matches Stata jwdid method(logit): logit y [treatment_interactions] + i.gvar i.tvar — cohort main effects + time main effects (additive), + not cohort×time saturated group FEs. + """ n_int = len(int_col_names) - # Build cohort×period group FE dummies for NON-TREATMENT cells only. - # Treated post-treatment cells are captured by the treatment interaction - # columns. Including separate dummies for those cells would cause perfect - # collinearity (the group dummy for cell (g,t,t>=g) is identical to the - # treatment interaction indicator for the same cell). - is_treatment_cell = np.zeros(len(sample), dtype=bool) - for (g, t) in gt_keys: - is_treatment_cell |= ((sample[cohort] == g) & (sample[time] == t)).values - - grp_label = ( - sample[cohort].astype(str) + "_" + sample[time].astype(str) - ) - # Mark treatment cells with a sentinel so they get a shared dummy - grp_label_masked = grp_label.copy() - grp_label_masked[is_treatment_cell] = "__treated__" - group_dummies = pd.get_dummies(grp_label_masked, drop_first=True).values.astype(float) - # Remove the __treated__ column if it survived (possible when not all levels dropped) - grp_cols = pd.get_dummies(grp_label_masked, drop_first=True).columns.tolist() - if "__treated__" in grp_cols: - treated_col_idx = grp_cols.index("__treated__") - group_dummies = np.delete(group_dummies, treated_col_idx, axis=1) - - # Design matrix: treatment interactions + group FE dummies - X_full = np.hstack([X_int, group_dummies]) + # Design matrix: treatment interactions + cohort FEs + time FEs + # This matches Stata's `i.gvar i.tvar` specification. + cohort_dummies = pd.get_dummies( + sample[cohort], drop_first=True + ).values.astype(float) + time_dummies = pd.get_dummies( + sample[time], drop_first=True + ).values.astype(float) + X_full = np.hstack([X_int, cohort_dummies, time_dummies]) y = sample[outcome].values.astype(float) cluster_col = self.cluster if self.cluster else unit @@ -574,20 +564,21 @@ def _fit_logit( if cell_mask.sum() == 0: continue eta_base = X_with_intercept[cell_mask] @ beta - att = float(np.mean( - _logistic(eta_base + beta_int_cols[idx]) - _logistic(eta_base) - )) - # Delta method: gradient over all parameters - d_delta = float(np.mean(_logistic_deriv(eta_base + beta_int_cols[idx]))) - d_base_diff = ( - _logistic_deriv(eta_base + beta_int_cols[idx]) - - _logistic_deriv(eta_base) - ) + # eta_base already contains the treatment effect (D_{g,t}=1 in cell). + # Counterfactual: eta_0 = eta_base - delta (treatment switched off). + # ATT = E[Λ(η_1)] - E[Λ(η_0)] = E[Λ(η_base)] - E[Λ(η_base - δ)] + delta = beta_int_cols[idx] + eta_0 = eta_base - delta + att = float(np.mean(_logistic(eta_base) - _logistic(eta_0))) + # Delta method gradient: d(ATT)/d(β) + # for p ≠ int_idx: mean_i[(Λ'(η_1) - Λ'(η_0)) * X_p] + # for p = int_idx: mean_i[Λ'(η_1)] + d_diff = _logistic_deriv(eta_base) - _logistic_deriv(eta_0) grad = np.mean( - X_with_intercept[cell_mask] * d_base_diff[:, None], + X_with_intercept[cell_mask] * d_diff[:, None], axis=0 ) - grad[1 + idx] += d_delta + grad[1 + idx] = float(np.mean(_logistic_deriv(eta_base))) se = float(np.sqrt(max(grad @ vcov_full @ grad, 0.0))) t_stat, p_value, conf_int = safe_inference(att, se, alpha=self.alpha) gt_effects[(g, t)] = { @@ -598,6 +589,13 @@ def _fit_logit( gt_grads[(g, t)] = grad gt_keys_ordered = [k for k in gt_keys if k in gt_effects] + # ATT-level covariance: J @ vcov_full @ J' where J rows are per-cell gradients + if gt_keys_ordered: + J = np.array([gt_grads[k] for k in gt_keys_ordered]) + gt_vcov = J @ vcov_full @ J.T + else: + gt_vcov = None + # Overall SE via joint delta method: ∇β(overall_att) = Σ w_k/w_total * grad_k post_keys = [(g, t) for (g, t) in gt_keys_ordered if t >= g] w_total = sum(gt_weights.get(k, 0) for k in post_keys) @@ -630,7 +628,7 @@ def _fit_logit( n_control_units=int(sample[sample[cohort] == 0][unit].nunique()), alpha=self.alpha, _gt_weights=gt_weights, - _gt_vcov=None, + _gt_vcov=gt_vcov, _gt_keys=gt_keys_ordered, ) @@ -647,30 +645,26 @@ def _fit_poisson( int_col_names: List[str], groups: List[Any], ) -> WooldridgeDiDResults: - """Poisson path: cohort×period group FE + solve_poisson + ASF ATT.""" - n_int = len(int_col_names) + """Poisson path: cohort + time additive FEs + solve_poisson + ASF ATT. - # Build group FE dummies for NON-TREATMENT cells only (avoids collinearity; - # see _fit_logit for detailed explanation). - is_treatment_cell = np.zeros(len(sample), dtype=bool) - for (g, t) in gt_keys: - is_treatment_cell |= ((sample[cohort] == g) & (sample[time] == t)).values + Matches Stata jwdid method(poisson): poisson y [treatment_interactions] + i.gvar i.tvar — cohort main effects + time main effects (additive), + not cohort×time saturated group FEs. + """ + n_int = len(int_col_names) - grp_label = ( - sample[cohort].astype(str) + "_" + sample[time].astype(str) - ) - grp_label_masked = grp_label.copy() - grp_label_masked[is_treatment_cell] = "__treated__" - _dummy_df = pd.get_dummies(grp_label_masked, drop_first=True) - group_dummies = _dummy_df.values.astype(float) - if "__treated__" in _dummy_df.columns: - treated_col_idx = _dummy_df.columns.tolist().index("__treated__") - group_dummies = np.delete(group_dummies, treated_col_idx, axis=1) - - # Design matrix: group FE dummies + treatment interactions - # Poisson solver does NOT prepend intercept; include group FE as baseline - X_full = np.hstack([group_dummies, X_int]) - n_fe = group_dummies.shape[1] + # Design matrix: intercept + treatment interactions + cohort FEs + time FEs. + # Matches Stata's `i.gvar i.tvar` + treatment interaction specification. + # solve_poisson does not prepend an intercept, so we include one explicitly. + intercept = np.ones((len(sample), 1)) + cohort_dummies = pd.get_dummies( + sample[cohort], drop_first=True + ).values.astype(float) + time_dummies = pd.get_dummies( + sample[time], drop_first=True + ).values.astype(float) + X_full = np.hstack([intercept, X_int, cohort_dummies, time_dummies]) + # Treatment interaction coefficients start at column index 1. y = sample[outcome].values.astype(float) cluster_col = self.cluster if self.cluster else unit @@ -696,10 +690,13 @@ def _fit_poisson( meat += np.outer(scores_c, scores_c) vcov_full = XtWX_inv @ meat @ XtWX_inv - # Interaction columns start at column n_fe in X_full - beta_int = beta[n_fe: n_fe + n_int] + # Treatment interaction coefficients: beta[1 : 1+n_int] + beta_int = beta[1: 1 + n_int] - # ASF ATT(g,t): E[exp(η + δ) - exp(η)] for treated units in cell + # ASF ATT(g,t) for treated units in each cell. + # eta_base = X_full @ beta already includes the treatment effect (D_{g,t}=1). + # Counterfactual: eta_0 = eta_base - delta (treatment switched off). + # ATT = E[exp(η_1)] - E[exp(η_0)] = E[exp(η_base)] - E[exp(η_base - δ)] gt_effects: Dict[Tuple, Dict] = {} gt_weights: Dict[Tuple, int] = {} gt_grads: Dict[Tuple, np.ndarray] = {} # per-cell gradients for aggregate SE @@ -709,19 +706,18 @@ def _fit_poisson( cell_mask = (sample[cohort] == g) & (sample[time] == t) if cell_mask.sum() == 0: continue - eta_base = X_full[cell_mask] @ beta + eta_base = np.clip(X_full[cell_mask] @ beta, -500, 500) delta = beta_int[idx] - att = float(np.mean(np.exp(eta_base + delta) - np.exp(eta_base))) - # Delta method gradient - grad_delta = float(np.mean(np.exp(eta_base + delta))) - grad_base = np.mean( - X_full[cell_mask] * ( - np.exp(eta_base + delta) - np.exp(eta_base) - )[:, None], - axis=0, - ) - grad = grad_base.copy() - grad[n_fe + idx] += grad_delta + eta_0 = eta_base - delta + mu_1 = np.exp(eta_base) + mu_0 = np.exp(eta_0) + att = float(np.mean(mu_1 - mu_0)) + # Delta method gradient: + # for p ≠ int_idx: mean_i[(μ_1 - μ_0) * X_p] + # for p = int_idx: mean_i[μ_1] + diff_mu = mu_1 - mu_0 + grad = np.mean(X_full[cell_mask] * diff_mu[:, None], axis=0) + grad[1 + idx] = float(np.mean(mu_1)) se = float(np.sqrt(max(grad @ vcov_full @ grad, 0.0))) t_stat, p_value, conf_int = safe_inference(att, se, alpha=self.alpha) gt_effects[(g, t)] = { @@ -732,6 +728,13 @@ def _fit_poisson( gt_grads[(g, t)] = grad gt_keys_ordered = [k for k in gt_keys if k in gt_effects] + # ATT-level covariance: J @ vcov_full @ J' where J rows are per-cell gradients + if gt_keys_ordered: + J = np.array([gt_grads[k] for k in gt_keys_ordered]) + gt_vcov = J @ vcov_full @ J.T + else: + gt_vcov = None + # Overall SE via joint delta method post_keys = [(g, t) for (g, t) in gt_keys_ordered if t >= g] w_total = sum(gt_weights.get(k, 0) for k in post_keys) @@ -764,6 +767,6 @@ def _fit_poisson( n_control_units=int(sample[sample[cohort] == 0][unit].nunique()), alpha=self.alpha, _gt_weights=gt_weights, - _gt_vcov=None, + _gt_vcov=gt_vcov, _gt_keys=gt_keys_ordered, ) diff --git a/docs/api/index.rst b/docs/api/index.rst index 5a57ee66..437d5222 100644 --- a/docs/api/index.rst +++ b/docs/api/index.rst @@ -24,6 +24,7 @@ Core estimator classes for DiD analysis: diff_diff.TROP diff_diff.ContinuousDiD diff_diff.EfficientDiD + diff_diff.WooldridgeDiD Results Classes --------------- @@ -52,6 +53,7 @@ Result containers returned by estimators: diff_diff.DoseResponseCurve diff_diff.EfficientDiDResults diff_diff.EDiDBootstrapResults + diff_diff.wooldridge_results.WooldridgeDiDResults Visualization ------------- @@ -199,6 +201,7 @@ Detailed documentation by module: trop continuous_did efficient_did + wooldridge_etwfe results visualization diagnostics diff --git a/docs/api/wooldridge_etwfe.rst b/docs/api/wooldridge_etwfe.rst new file mode 100644 index 00000000..a2e643dc --- /dev/null +++ b/docs/api/wooldridge_etwfe.rst @@ -0,0 +1,169 @@ +Wooldridge Extended Two-Way Fixed Effects (ETWFE) +=================================================== + +Extended Two-Way Fixed Effects estimator from Wooldridge (2021, 2023), +equivalent to the Stata ``jwdid`` package (Friosavila 2021). + +This module implements ETWFE via a single saturated regression that: + +1. **Estimates ATT(g,t)** for each cohort×time treatment cell simultaneously +2. **Supports linear (OLS), Poisson QMLE, and logit** link functions +3. **Uses ASF-based ATT** for nonlinear models: E[f(η₁)] − E[f(η₀)] +4. **Computes delta-method SEs** for all aggregations (event, group, calendar, simple) +5. **Matches Stata jwdid** output exactly for both OLS and nonlinear paths + +**When to use WooldridgeDiD:** + +- Staggered adoption design with heterogeneous treatment timing +- Nonlinear outcomes (binary, count, non-negative continuous) +- You want a single-regression approach matching Stata's ``jwdid`` +- You need event-study, group, calendar, or simple ATT aggregations + +**References:** + +- Wooldridge, J. M. (2021). Two-Way Fixed Effects, the Two-Way Mundlak + Regression, and Difference-in-Differences Estimators. *SSRN 3906345*. +- Wooldridge, J. M. (2023). Simple approaches to nonlinear + difference-in-differences with panel data. *The Econometrics Journal*, + 26(3), C31–C66. +- Friosavila, F. (2021). ``jwdid``: Stata module for ETWFE. SSC s459114. + +.. module:: diff_diff.wooldridge + +WooldridgeDiD +-------------- + +Main estimator class for Wooldridge ETWFE. + +.. autoclass:: diff_diff.WooldridgeDiD + :members: + :undoc-members: + :show-inheritance: + + .. rubric:: Methods + + .. autosummary:: + + ~WooldridgeDiD.fit + ~WooldridgeDiD.get_params + ~WooldridgeDiD.set_params + +WooldridgeDiDResults +--------------------- + +Results container returned by ``WooldridgeDiD.fit()``. + +.. autoclass:: diff_diff.wooldridge_results.WooldridgeDiDResults + :members: + :undoc-members: + :show-inheritance: + + .. rubric:: Methods + + .. autosummary:: + + ~WooldridgeDiDResults.aggregate + ~WooldridgeDiDResults.summary + +Example Usage +------------- + +Basic OLS (matches Stata ``jwdid y, ivar(unit) tvar(time) gvar(cohort)``):: + + import pandas as pd + from diff_diff import WooldridgeDiD + + df = pd.read_stata("mpdta.dta") + df['first_treat'] = df['first_treat'].astype(int) + + m = WooldridgeDiD() + r = m.fit(df, outcome='lemp', unit='countyreal', time='year', cohort='first_treat') + + r.aggregate('event').aggregate('group').aggregate('simple') + print(r.summary('event')) + print(r.summary('group')) + print(r.summary('simple')) + +View cohort×time cell estimates (post-treatment):: + + for (g, t), v in sorted(r.group_time_effects.items()): + if t >= g: + print(f"g={g} t={t} ATT={v['att']:.4f} SE={v['se']:.4f}") + +Poisson QMLE for non-negative outcomes +(matches Stata ``jwdid emp, method(poisson)``):: + + import numpy as np + df['emp'] = np.exp(df['lemp']) + + m_pois = WooldridgeDiD(method='poisson') + r_pois = m_pois.fit(df, outcome='emp', unit='countyreal', + time='year', cohort='first_treat') + r_pois.aggregate('event').aggregate('group').aggregate('simple') + print(r_pois.summary('simple')) + +Logit for binary outcomes +(matches Stata ``jwdid y, method(logit)``):: + + m_logit = WooldridgeDiD(method='logit') + r_logit = m_logit.fit(df, outcome='hi_emp', unit='countyreal', + time='year', cohort='first_treat') + r_logit.aggregate('group').aggregate('simple') + print(r_logit.summary('group')) + +Aggregation Methods +------------------- + +Call ``.aggregate(type)`` before ``.summary(type)``: + +.. list-table:: + :header-rows: 1 + :widths: 15 30 25 + + * - Type + - Description + - Stata equivalent + * - ``'event'`` + - ATT by relative time k = t − g + - ``estat event`` + * - ``'group'`` + - ATT averaged across post-treatment periods per cohort + - ``estat group`` + * - ``'calendar'`` + - ATT averaged across cohorts per calendar period + - ``estat calendar`` + * - ``'simple'`` + - Overall weighted average ATT + - ``estat simple`` + +Comparison with Other Staggered Estimators +------------------------------------------ + +.. list-table:: + :header-rows: 1 + :widths: 20 27 27 26 + + * - Feature + - WooldridgeDiD (ETWFE) + - CallawaySantAnna + - ImputationDiD + * - Approach + - Single saturated regression + - Separate 2×2 DiD per cell + - Impute Y(0) via FE model + * - Nonlinear outcomes + - Yes (Poisson, Logit) + - No + - No + * - Covariates + - Via regression (linear index) + - OR, IPW, DR + - Supported + * - SE for aggregations + - Delta method + - Multiplier bootstrap + - Multiplier bootstrap + * - Stata equivalent + - ``jwdid`` + - ``csdid`` + - ``did_imputation`` diff --git a/docs/tutorials/16_wooldridge_etwfe.ipynb b/docs/tutorials/16_wooldridge_etwfe.ipynb new file mode 100644 index 00000000..9fdd8a07 --- /dev/null +++ b/docs/tutorials/16_wooldridge_etwfe.ipynb @@ -0,0 +1,605 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a1b2c3d4", + "metadata": {}, + "source": [ + "# Wooldridge Extended Two-Way Fixed Effects (ETWFE)\n", + "\n", + "This tutorial demonstrates the `WooldridgeDiD` estimator (alias: `ETWFE`), which implements Wooldridge's (2021, 2023) Extended Two-Way Fixed Effects approach — the basis of the Stata `jwdid` package.\n", + "\n", + "**What ETWFE does:** Estimates cohort×time Average Treatment Effects (ATT(g,t)) via a single saturated regression that interacts treatment indicators with cohort×time cells. Unlike standard TWFE, it correctly handles heterogeneous treatment effects across cohorts and time periods. The key insight is to include all cohort×time interaction terms simultaneously, with additive cohort and time fixed effects.\n", + "\n", + "**Key features:**\n", + "- Matches Stata `jwdid` output exactly (OLS and nonlinear paths)\n", + "- Supports **linear (OLS)**, **Poisson**, and **logit** link functions\n", + "- Nonlinear ATTs use the Average Structural Function (ASF): E[f(η₁)] − E[f(η₀)]\n", + "- Delta-method standard errors for all aggregations\n", + "- Cluster-robust sandwich variance\n", + "\n", + "**Topics covered:**\n", + "1. Basic OLS estimation\n", + "2. Cohort×time cell estimates ATT(g,t)\n", + "3. Aggregation: event-study, group, simple\n", + "4. Poisson QMLE for count / non-negative outcomes\n", + "5. Logit for binary outcomes\n", + "6. Comparison with Callaway-Sant'Anna\n", + "7. Parameter reference and guidance\n", + "\n", + "*Prerequisites: [Tutorial 02](02_staggered_did.ipynb) (Staggered DiD).*\n", + "\n", + "*See also: [Tutorial 15](15_efficient_did.ipynb) for Efficient DiD, [Tutorial 11](11_imputation_did.ipynb) for Imputation DiD.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b2c3d4e5", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from diff_diff import WooldridgeDiD, CallawaySantAnna, generate_staggered_data\n", + "\n", + "try:\n", + " import matplotlib.pyplot as plt\n", + " plt.style.use('seaborn-v0_8-whitegrid')\n", + " HAS_MATPLOTLIB = True\n", + "except ImportError:\n", + " HAS_MATPLOTLIB = False\n", + " print(\"matplotlib not installed - visualization examples will be skipped\")" + ] + }, + { + "cell_type": "markdown", + "id": "c3d4e5f6", + "metadata": {}, + "source": [ + "## Data Setup\n", + "\n", + "We use `generate_staggered_data()` to create a balanced panel with 3 treatment cohorts, a never-treated group, and a known ATT of 2.0. This makes it easy to verify estimation accuracy.\n", + "\n", + "We also demonstrate with the **mpdta** dataset (Callaway & Sant'Anna 2021), which contains county-level log employment data with staggered minimum-wage adoption — the canonical benchmark for staggered DiD methods." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d4e5f6a7", + "metadata": {}, + "outputs": [], + "source": [ + "# Simulated data\n", + "data = generate_staggered_data(\n", + " n_units=300, n_periods=10, treatment_effect=2.0,\n", + " dynamic_effects=False, seed=42\n", + ")\n", + "\n", + "print(f\"Shape: {data.shape}\")\n", + "print(f\"Cohorts: {sorted(data['first_treat'].unique())}\")\n", + "print(f\"Periods: {sorted(data['period'].unique())}\")\n", + "print()\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "id": "e5f6a7b8", + "metadata": {}, + "source": [ + "## Basic OLS Estimation\n", + "\n", + "The default `method='ols'` fits a single regression with:\n", + "- Treatment interaction dummies (one per treatment cohort × post-treatment period cell)\n", + "- Additive cohort fixed effects (`i.gvar` in Stata)\n", + "- Additive time fixed effects (`i.tvar` in Stata)\n", + "\n", + "This matches Stata's `jwdid y, ivar(unit) tvar(time) gvar(cohort)`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f6a7b8c9", + "metadata": {}, + "outputs": [], + "source": [ + "m = WooldridgeDiD() # default: method='ols'\n", + "r = m.fit(data, outcome='outcome', unit='unit', time='period', cohort='first_treat')\n", + "\n", + "# Compute aggregations\n", + "r.aggregate('event').aggregate('group').aggregate('simple')\n", + "\n", + "print(r.summary('simple'))" + ] + }, + { + "cell_type": "markdown", + "id": "a7b8c9d0", + "metadata": {}, + "source": [ + "## Cohort×Time Cell Estimates ATT(g,t)\n", + "\n", + "The raw building blocks are ATT(g,t) — the treatment effect for cohort `g` at calendar time `t`. These are stored in `r.group_time_effects` and correspond to Stata's regression output table (`first_treat#year#c.__tr__`).\n", + "\n", + "Post-treatment cells have `t >= g`; pre-treatment cells (`t < g`) serve as placebo checks." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b8c9d0e1", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Post-treatment ATT(g,t) cells\")\n", + "print(\"{:>8} {:>8} | {:>10} {:>10} {:>7} {:>7}\".format(\n", + " \"cohort\", \"year\", \"Coef.\", \"Std.Err.\", \"t\", \"P>|t|\"))\n", + "print(\"-\" * 60)\n", + "\n", + "for (g, t), v in sorted(r.group_time_effects.items()):\n", + " if t < g:\n", + " continue\n", + " row = \"{:>8} {:>8} | {:>10.4f} {:>10.4f} {:>7.2f} {:>7.3f}\".format(\n", + " int(g), int(t), v['att'], v['se'], v['t_stat'], v['p_value']\n", + " )\n", + " print(row)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c9d0e1f2", + "metadata": {}, + "outputs": [], + "source": [ + "# Also show pre-treatment placebo cells\n", + "print(\"Pre-treatment placebo ATT(g,t) cells (should be ~0 under parallel trends)\")\n", + "print(\"{:>8} {:>8} | {:>10} {:>10} {:>7} {:>7}\".format(\n", + " \"cohort\", \"year\", \"Coef.\", \"Std.Err.\", \"t\", \"P>|t|\"))\n", + "print(\"-\" * 60)\n", + "\n", + "for (g, t), v in sorted(r.group_time_effects.items()):\n", + " if t >= g:\n", + " continue\n", + " row = \"{:>8} {:>8} | {:>10.4f} {:>10.4f} {:>7.2f} {:>7.3f}\".format(\n", + " int(g), int(t), v['att'], v['se'], v['t_stat'], v['p_value']\n", + " )\n", + " print(row)" + ] + }, + { + "cell_type": "markdown", + "id": "d0e1f2a3", + "metadata": {}, + "source": [ + "## Aggregation Methods\n", + "\n", + "ETWFE supports four aggregation types, matching Stata's `estat` post-estimation commands:\n", + "\n", + "| Python | Stata | Description |\n", + "|--------|-------|-------------|\n", + "| `aggregate('event')` | `estat event` | By relative time k = t − g |\n", + "| `aggregate('group')` | `estat group` | By treatment cohort g |\n", + "| `aggregate('calendar')` | `estat calendar` | By calendar time t |\n", + "| `aggregate('simple')` | `estat simple` | Overall weighted average ATT |\n", + "\n", + "Standard errors use the delta method, propagating uncertainty from the cell-level ATT covariance matrix." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e1f2a3b4", + "metadata": {}, + "outputs": [], + "source": [ + "# Event-study aggregation: ATT by relative time k = t - g\n", + "print(r.summary('event'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f2a3b4c5", + "metadata": {}, + "outputs": [], + "source": [ + "# Group aggregation: ATT averaged across post-treatment periods for each cohort\n", + "print(r.summary('group'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a3b4c5d6", + "metadata": {}, + "outputs": [], + "source": [ + "# Simple ATT: overall weighted average\n", + "print(r.summary('simple'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b4c5d6e7", + "metadata": {}, + "outputs": [], + "source": [ + "# Event study plot\n", + "if HAS_MATPLOTLIB:\n", + " es = r.event_study_effects\n", + " ks = sorted(es.keys())\n", + " atts = [es[k]['att'] for k in ks]\n", + " lo = [es[k]['conf_int'][0] for k in ks]\n", + " hi = [es[k]['conf_int'][1] for k in ks]\n", + "\n", + " fig, ax = plt.subplots(figsize=(9, 5))\n", + " ax.errorbar(ks, atts, yerr=[np.array(atts) - np.array(lo), np.array(hi) - np.array(atts)],\n", + " fmt='o-', capsize=4, color='steelblue', label='ETWFE (OLS)')\n", + " ax.axhline(0, color='black', linestyle='--', linewidth=0.8)\n", + " ax.axvline(-0.5, color='red', linestyle=':', linewidth=0.8, label='Treatment onset')\n", + " ax.set_xlabel('Relative period (k = t − g)')\n", + " ax.set_ylabel('ATT')\n", + " ax.set_title('ETWFE Event Study')\n", + " ax.legend()\n", + " plt.tight_layout()\n", + " plt.show()\n", + "else:\n", + " print(\"Install matplotlib to see the event study plot: pip install matplotlib\")" + ] + }, + { + "cell_type": "markdown", + "id": "c5d6e7f8", + "metadata": {}, + "source": [ + "## Poisson QMLE for Count / Non-Negative Outcomes\n", + "\n", + "`method='poisson'` fits a Poisson QMLE regression. This is valid for any non-negative continuous outcome, not just count data — the Poisson log-likelihood produces consistent estimates whenever the conditional mean is correctly specified as exp(Xβ).\n", + "\n", + "The ATT is computed as the **Average Structural Function (ASF) difference**:\n", + "\n", + "$$\\text{ATT}(g,t) = \\frac{1}{N_{g,t}} \\sum_{i \\in g,t} \\left[\\exp(\\eta_{i,1}) - \\exp(\\eta_{i,0})\\right]$$\n", + "\n", + "where η₁ = Xβ (with treatment) and η₀ = Xβ − δ (counterfactual without treatment).\n", + "\n", + "This matches Stata's `jwdid y, method(poisson)`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d6e7f8a9", + "metadata": {}, + "outputs": [], + "source": [ + "# Simulate a non-negative outcome (e.g., employment level)\n", + "data_pois = data.copy()\n", + "data_pois['emp'] = np.exp(data_pois['outcome'] / 4 + 3) # positive outcome\n", + "\n", + "m_pois = WooldridgeDiD(method='poisson')\n", + "r_pois = m_pois.fit(data_pois, outcome='emp', unit='unit', time='period', cohort='first_treat')\n", + "r_pois.aggregate('event').aggregate('group').aggregate('simple')\n", + "\n", + "print(r_pois.summary('simple'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e7f8a9b0", + "metadata": {}, + "outputs": [], + "source": [ + "# Cohort×time cells (post-treatment, Poisson)\n", + "print(\"Poisson ATT(g,t) — post-treatment cells\")\n", + "print(\"{:>8} {:>8} | {:>10} {:>10} {:>7} {:>7}\".format(\n", + " \"cohort\", \"year\", \"ATT\", \"Std.Err.\", \"t\", \"P>|t|\"))\n", + "print(\"-\" * 60)\n", + "\n", + "for (g, t), v in sorted(r_pois.group_time_effects.items()):\n", + " if t < g:\n", + " continue\n", + " print(\"{:>8} {:>8} | {:>10.4f} {:>10.4f} {:>7.2f} {:>7.3f}\".format(\n", + " int(g), int(t), v['att'], v['se'], v['t_stat'], v['p_value']\n", + " ))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f8a9b0c1", + "metadata": {}, + "outputs": [], + "source": [ + "print(r_pois.summary('event'))\n", + "print(r_pois.summary('group'))" + ] + }, + { + "cell_type": "markdown", + "id": "a9b0c1d2", + "metadata": {}, + "source": [ + "## Logit for Binary Outcomes\n", + "\n", + "`method='logit'` fits a logit model and computes ATT as the ASF probability difference:\n", + "\n", + "$$\\text{ATT}(g,t) = \\frac{1}{N_{g,t}} \\sum_{i \\in g,t} \\left[\\Lambda(\\eta_{i,1}) - \\Lambda(\\eta_{i,0})\\right]$$\n", + "\n", + "where Λ(·) is the logistic function. Standard errors use the delta method.\n", + "\n", + "This matches Stata's `jwdid y, method(logit)`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b0c1d2e3", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a binary outcome\n", + "data_logit = data.copy()\n", + "median_val = data_logit.loc[data_logit['period'] == data_logit['period'].min(), 'outcome'].median()\n", + "data_logit['hi_outcome'] = (data_logit['outcome'] > median_val).astype(int)\n", + "\n", + "print(f\"Binary outcome mean: {data_logit['hi_outcome'].mean():.3f}\")\n", + "\n", + "m_logit = WooldridgeDiD(method='logit')\n", + "r_logit = m_logit.fit(data_logit, outcome='hi_outcome', unit='unit', time='period', cohort='first_treat')\n", + "r_logit.aggregate('event').aggregate('group').aggregate('simple')\n", + "\n", + "print(r_logit.summary('simple'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c1d2e3f4", + "metadata": {}, + "outputs": [], + "source": [ + "print(r_logit.summary('group'))" + ] + }, + { + "cell_type": "markdown", + "id": "d2e3f4a5", + "metadata": {}, + "source": [ + "## mpdta: Real-World Example\n", + "\n", + "The **mpdta** dataset (Callaway & Sant'Anna 2021) contains county-level log employment (`lemp`) data with staggered minimum-wage adoption (`first_treat` = year of treatment, 0 = never treated). It is the canonical benchmark for staggered DiD methods.\n", + "\n", + "This replicates Stata's `jwdid lemp, ivar(countyreal) tvar(year) gvar(first_treat)` and `jwdid lemp, ivar(countyreal) tvar(year) gvar(first_treat) method(poisson)` exactly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3f4a5b6", + "metadata": {}, + "outputs": [], + "source": [ + "# Load mpdta (adjust path as needed)\n", + "try:\n", + " mpdta = pd.read_stata(\"mpdta.dta\")\n", + " mpdta['first_treat'] = mpdta['first_treat'].astype(int)\n", + " mpdta['countyreal'] = mpdta['countyreal'].astype(int)\n", + " mpdta['year'] = mpdta['year'].astype(int)\n", + " HAS_MPDTA = True\n", + " print(f\"mpdta loaded: {mpdta.shape}\")\n", + " print(f\"Cohorts: {sorted(mpdta['first_treat'].unique())}\")\n", + "except FileNotFoundError:\n", + " HAS_MPDTA = False\n", + " print(\"mpdta.dta not found — skipping real-data cells.\")\n", + " print(\"Download from: https://github.com/bcallaway11/did (R package data)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f4a5b6c7", + "metadata": {}, + "outputs": [], + "source": [ + "if HAS_MPDTA:\n", + " # OLS — matches: jwdid lemp, ivar(countyreal) tvar(year) gvar(first_treat)\n", + " m_ols = WooldridgeDiD(method='ols')\n", + " r_ols = m_ols.fit(mpdta, outcome='lemp', unit='countyreal', time='year', cohort='first_treat')\n", + " r_ols.aggregate('event').aggregate('group').aggregate('simple')\n", + " print(r_ols.summary('event'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a5b6c7d8", + "metadata": {}, + "outputs": [], + "source": [ + "if HAS_MPDTA:\n", + " # cohort x time ATT cells (post-treatment)\n", + " # Matches Stata: first_treat#year#c.__tr__ output table\n", + " print(\"ATT(g,t) — post-treatment cells (matches Stata jwdid output)\")\n", + " print(\"{:>6} {:>6} | {:>9} {:>9} {:>7} {:>7}\".format(\n", + " \"cohort\", \"year\", \"Coef.\", \"Std.Err.\", \"t\", \"P>|t|\"))\n", + " print(\"-\" * 55)\n", + " for (g, t), v in sorted(r_ols.group_time_effects.items()):\n", + " if t < g:\n", + " continue\n", + " print(\"{:>6} {:>6} | {:>9.4f} {:>9.4f} {:>7.2f} {:>7.3f}\".format(\n", + " g, t, v['att'], v['se'], v['t_stat'], v['p_value']))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b6c7d8e9", + "metadata": {}, + "outputs": [], + "source": [ + "if HAS_MPDTA:\n", + " # Poisson — matches: gen emp=exp(lemp) / jwdid emp, method(poisson)\n", + " mpdta['emp'] = np.exp(mpdta['lemp'])\n", + "\n", + " m_pois2 = WooldridgeDiD(method='poisson')\n", + " r_pois2 = m_pois2.fit(mpdta, outcome='emp', unit='countyreal', time='year', cohort='first_treat')\n", + " r_pois2.aggregate('event').aggregate('group').aggregate('simple')\n", + "\n", + " print(r_pois2.summary('event'))\n", + " print(r_pois2.summary('group'))\n", + " print(r_pois2.summary('simple'))" + ] + }, + { + "cell_type": "markdown", + "id": "c7d8e9f0", + "metadata": {}, + "source": [ + "## Comparison with Callaway-Sant'Anna\n", + "\n", + "ETWFE and Callaway-Sant'Anna are both valid for staggered designs. Under homogeneous treatment effects and additive parallel trends, they should produce similar ATT(g,t) point estimates. Key differences:\n", + "\n", + "| Aspect | WooldridgeDiD (ETWFE) | CallawaySantAnna |\n", + "|--------|----------------------|------------------|\n", + "| Approach | Single saturated regression | Separate 2×2 DiD per cell |\n", + "| Nonlinear outcomes | Yes (Poisson, Logit) | No |\n", + "| Covariates | Via regression (linear index) | OR, IPW, DR |\n", + "| SE for aggregations | Delta method | Multiplier bootstrap |\n", + "| Stata equivalent | `jwdid` | `csdid` |" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d8e9f0a1", + "metadata": {}, + "outputs": [], + "source": [ + "# Compare overall ATT: ETWFE vs Callaway-Sant'Anna\n", + "cs = CallawaySantAnna()\n", + "r_cs = cs.fit(data, outcome='outcome', unit='unit', time='period', first_treat='first_treat')\n", + "\n", + "m_etwfe = WooldridgeDiD(method='ols')\n", + "r_etwfe = m_etwfe.fit(data, outcome='outcome', unit='unit', time='period', cohort='first_treat')\n", + "r_etwfe.aggregate('simple')\n", + "\n", + "print(\"Overall ATT Comparison (true effect = 2.0)\")\n", + "print(\"=\" * 60)\n", + "print(\"{:<25} {:>10} {:>10} {:>12}\".format(\"Estimator\", \"ATT\", \"SE\", \"95% CI\"))\n", + "print(\"-\" * 60)\n", + "\n", + "for name, est_r in [(\"WooldridgeDiD (ETWFE)\", r_etwfe), (\"CallawaySantAnna\", r_cs)]:\n", + " ci = est_r.overall_conf_int\n", + " print(\"{:<25} {:>10.4f} {:>10.4f} [{:.3f}, {:.3f}]\".format(\n", + " name, est_r.overall_att, est_r.overall_se, ci[0], ci[1]\n", + " ))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e9f0a1b2", + "metadata": {}, + "outputs": [], + "source": [ + "# Event-study comparison\n", + "r_cs_es = CallawaySantAnna().fit(\n", + " data, outcome='outcome', unit='unit', time='period',\n", + " first_treat='first_treat', aggregate='event_study'\n", + ")\n", + "\n", + "if HAS_MATPLOTLIB:\n", + " es_etwfe = r_etwfe.event_study_effects\n", + " es_cs = {int(row['relative_period']): row\n", + " for _, row in r_cs_es.to_dataframe(level='event_study').iterrows()}\n", + "\n", + " ks = sorted(es_etwfe.keys())\n", + "\n", + " fig, ax = plt.subplots(figsize=(10, 5))\n", + " offset = 0.1\n", + "\n", + " atts_e = [es_etwfe[k]['att'] for k in ks]\n", + " lo_e = [es_etwfe[k]['conf_int'][0] for k in ks]\n", + " hi_e = [es_etwfe[k]['conf_int'][1] for k in ks]\n", + " ax.errorbar([k - offset for k in ks], atts_e,\n", + " yerr=[np.array(atts_e) - np.array(lo_e), np.array(hi_e) - np.array(atts_e)],\n", + " fmt='o-', capsize=4, color='steelblue', label='ETWFE')\n", + "\n", + " ks_cs = sorted(es_cs.keys())\n", + " atts_cs = [es_cs[k]['effect'] for k in ks_cs]\n", + " lo_cs = [es_cs[k]['conf_int_lower'] for k in ks_cs]\n", + " hi_cs = [es_cs[k]['conf_int_upper'] for k in ks_cs]\n", + " ax.errorbar([k + offset for k in ks_cs], atts_cs,\n", + " yerr=[np.array(atts_cs) - np.array(lo_cs), np.array(hi_cs) - np.array(atts_cs)],\n", + " fmt='s--', capsize=4, color='darkorange', label='Callaway-Sant\\'Anna')\n", + "\n", + " ax.axhline(0, color='black', linestyle='--', linewidth=0.8)\n", + " ax.axvline(-0.5, color='red', linestyle=':', linewidth=0.8)\n", + " ax.set_xlabel('Relative period (k = t − g)')\n", + " ax.set_ylabel('ATT')\n", + " ax.set_title('Event Study: ETWFE vs Callaway-Sant\\'Anna')\n", + " ax.legend()\n", + " plt.tight_layout()\n", + " plt.show()\n", + "else:\n", + " print(\"Install matplotlib to see the comparison plot: pip install matplotlib\")" + ] + }, + { + "cell_type": "markdown", + "id": "f0a1b2c3", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "**Key takeaways:**\n", + "\n", + "1. **ETWFE via a single regression**: all ATT(g,t) cells estimated jointly, not separately — computationally efficient and internally consistent\n", + "2. **OLS path** matches Stata `jwdid` exactly: additive cohort + time FEs, treatment interaction dummies\n", + "3. **Nonlinear paths** (Poisson, Logit) use the ASF formula: E[f(η₁)] − E[f(η₀)] — the only valid ATT definition for nonlinear models\n", + "4. **Four aggregations** mirror Stata's `estat` commands: event, group, calendar, simple\n", + "5. **Delta-method SEs** for all aggregations, including nonlinear paths\n", + "6. **When to prefer ETWFE**: nonlinear outcomes, or when a single-regression framework is preferred\n", + "7. **When to prefer CS/ImputationDiD**: covariate adjustment via IPW/DR, or multiplier bootstrap inference\n", + "\n", + "**Parameter reference:**\n", + "\n", + "| Parameter | Default | Description |\n", + "|-----------|---------|-------------|\n", + "| `method` | `'ols'` | `'ols'`, `'poisson'`, or `'logit'` |\n", + "| `control_group` | `'not_yet_treated'` | `'not_yet_treated'` or `'never_treated'` |\n", + "| `anticipation` | `0` | Anticipation periods before treatment |\n", + "| `alpha` | `0.05` | Significance level |\n", + "| `cluster` | `None` | Column for clustering (default: unit variable) |\n", + "\n", + "**References:**\n", + "- Wooldridge, J. M. (2021). Two-Way Fixed Effects, the Two-Way Mundlak Regression, and Difference-in-Differences Estimators. *SSRN 3906345*.\n", + "- Wooldridge, J. M. (2023). Simple approaches to nonlinear difference-in-differences with panel data. *The Econometrics Journal*, 26(3), C31–C66.\n", + "- Friosavila, F. (2021). `jwdid`: Stata module for ETWFE. SSC s459114.\n", + "\n", + "*See also: [Tutorial 02](02_staggered_did.ipynb) for Callaway-Sant'Anna, [Tutorial 15](15_efficient_did.ipynb) for Efficient DiD.*" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/tutorials/README.md b/docs/tutorials/README.md index 201da9b4..80ea5008 100644 --- a/docs/tutorials/README.md +++ b/docs/tutorials/README.md @@ -51,6 +51,16 @@ Efficient Difference-in-Differences (Chen, Sant'Anna & Xie 2025): - Event study and group-level aggregation - Bootstrap inference and diagnostics +### 16. Wooldridge ETWFE (`16_wooldridge_etwfe.ipynb`) +Extended Two-Way Fixed Effects (Wooldridge 2021/2023), equivalent to Stata `jwdid`: +- Basic OLS estimation matching Stata `jwdid` output exactly +- Cohort×time ATT(g,t) cell estimates +- Four aggregation methods: event-study, group, calendar, simple +- Poisson QMLE for count / non-negative outcomes +- Logit for binary outcomes +- Real-world mpdta example (staggered minimum-wage adoption) +- Comparison with Callaway-Sant'Anna + ## Running the Notebooks 1. 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