PR-A: WooldridgeDiD primary-source review (Wooldridge 2025 EmpEcon)

TODO.md

@@ -92,7 +92,7 @@ Deferred items from PR reviews that were not addressed before merge.
 | HonestDiD Delta^RM: uses naive FLCI instead of paper's ARP conditional/hybrid confidence sets (Sections 3.2.1-3.2.2). ARP infrastructure exists but moment inequality transformation needs calibration. CIs are conservative (wider, valid coverage). | `honest_did.py` | #248 | Medium |
 | Replicate weight tests use Fay-like BRR perturbations (0.5/1.5), not true half-sample BRR. Add true BRR regressions per estimator family. Existing `test_survey_phase6.py` covers true BRR at the helper level. | `tests/test_replicate_weight_expansion.py` | #253 | Low |
 | WooldridgeDiD: QMLE sandwich uses `aweight` cluster-robust adjustment `(G/(G-1))*(n-1)/(n-k)` vs Stata's `G/(G-1)` only. Conservative (inflates SEs). Add `qmle` weight type if Stata golden values confirm material difference. | `wooldridge.py`, `linalg.py` | #216 | Medium |
-| WooldridgeDiD: aggregation weights use cell-level n_{g,t} counts. Paper (W2025 Eqs. 7.2-7.4) defines cohort-share weights. Add optional `weights="cohort_share"` parameter to `aggregate()`. | `wooldridge_results.py` | #216 | Medium |
+| WooldridgeDiD: aggregation weights use cell-level n_{g,t} counts on BOTH the simple-overall path (paper W2025 Eqs. 7.2-7.4) AND the event-time path (paper W2025 Eq. 7.6 cohort-share-by-exposure `ω̂_{ge} = N_g / (N_q + ··· + N_{T-e})`). Both `simple` and `event` aggregations reuse the same `_gt_weights` cell-count array. Add optional `weights="cohort_share"` parameter to `aggregate()` covering both paths. | `wooldridge_results.py` | #216 | Medium |
 | WooldridgeDiD: optional *efficiency hint* (NOT a canonical-link violation per W2023 Prop 3.1) when method/outcome pairing is sub-optimal — e.g., `method="ols"` on binary data is consistent under QMLE, but `method="logit"` is typically more efficient. The original framing in this row as a "canonical link requirement" tied to Prop 3.1 was incorrect: Wooldridge (2023) Table 1 lists Gaussian/OLS for "any response" and logistic-Bernoulli for "binary OR fractional". A useful hint exists (efficiency), but should not be framed as a methodology violation. See PR #453 R1 review for the corrected reading. | `wooldridge.py` | #216 | Low |
 | WooldridgeDiD: Stata `jwdid` golden value tests — add R/Stata reference script and `TestReferenceValues` class. | `tests/test_wooldridge.py` | #216 | Medium |
 <!-- The PreTrendsPower R parity row (PR-C, 2026-05-19) and the four PR-A-tagged PreTrendsPower rows (CS/SA Σ_22 fidelity, helper `violation_weights`, custom-weight persistence, linear γ-unit MDV; resolved in PR-B 2026-05-18) are all closed — see CHANGELOG.md [Unreleased] Added/Changed/Fixed entries for the new behavior. -->

docs/methodology/REGISTRY.md

@@ -1492,7 +1492,7 @@ where `g(·)` is the link inverse (logistic or exp), `η_i` is the individual li
       ATT_overall = Σ_{(g,t): t≥g} n_{g,t} · ATT(g,t) / Σ_{(g,t): t≥g} n_{g,t}
 
   Cell weight `n_{g,t}` = count of obs in cohort g at time t in estimation sample.
-  - **Note:** Cell-level weighting (n_{g,t} observation counts) matches Stata `jwdid_estat` behavior. Differs from W2025 Eqs. 7.2-7.4 cohort-share weights that account for the number of post-treatment periods per cohort.
+  - **Note:** Cell-level weighting (n_{g,t} observation counts) matches Stata `jwdid_estat` behavior. Differs from W2025 Eqs. 7.2-7.4 cohort-share weights (simple-overall path) that account for the number of post-treatment periods per cohort, and also differs from W2025 Eq. 7.6 cohort-share-by-exposure weights `ω̂_{ge} = N_g / (N_q + ··· + N_{T-e})` (event-time path via `aggregate("event")`). Both `simple` and `event` aggregations reuse the same `_gt_weights` cell-count array, so the deviation applies uniformly across both paths. See `docs/methodology/papers/wooldridge-2025-review.md` § Deviations for context.
 
 - `group`: Weighted average across t for each cohort g
 - `calendar`: Weighted average across g for each calendar time t