Wooldridge Extended Two-Way Fixed Effects (ETWFE)#

Extended Two-Way Fixed Effects estimator from Wooldridge (2025, 2023), based on the Stata jwdid package specification (Friosavila 2021), with documented SE/aggregation deviations noted in the Methodology Registry.

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_study, group, calendar, simple)

  5. Supports paper W2025 cohort-share aggregation via aggregate(weights="cohort_share") (Eqs. 7.4 + 7.6; default is cell-count matching Stata jwdid_estat)

  6. Supports paper W2025 Section 8 heterogeneous cohort trends via cohort_trends=True (OLS path only; auto-routes to full-dummy mode; requires control_group="not_yet_treated" — the default — and survey_design=None; the never_treated and survey paths are fail-closed with NotImplementedError because the placebo-cell basis remaining collinear with the trend columns through the unit fixed effects / unvalidated survey-TSL composition would make the trend specification unidentified or unverified — see Methodology Registry for the full contract)

  7. Follows the Stata jwdid specification for OLS defaults and nonlinear paths (see Methodology Registry for documented SE/aggregation deviations)

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

  • You need paper W2025 cohort-share aggregation weights as an alternative to the default cell-count weighting

  • You need heterogeneous cohort-specific linear trends when parallel trends is violated (paper W2025 Section 8)

References:

  • Wooldridge, J. M. (2025). Two-way fixed effects, the two-way Mundlak regression, and difference-in-differences estimators. Empirical Economics, 69(5), 2545-2587. DOI 10.1007/s00181-025-02807-z.

  • 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.

WooldridgeDiD#

Main estimator class for Wooldridge ETWFE.

unsupported_period_action="drop" (default) removes periods lacking the required comparison support and warns. Use WooldridgeDiD(unsupported_period_action="error") to raise ValueError before those periods are removed; the error names the periods and affected observation count. This applies to all three methods, independently of rank_deficient_action. It does not control unidentified-cohort exclusion or relax other identification checks.

Support requires a positive-weight never-treated observation for OLS with control_group="never_treated". All other paths also admit observations before their cohort’s g - anticipation threshold. Unsupported periods can therefore occur even when never-treated units exist elsewhere in the panel. Existing pre-filter configuration, cohort, and survey-design checks retain precedence. Later checks, including covariate columns, nonlinear outcomes, and non-Conley explicit cluster columns, are not preflighted: an unsupported-period refusal can precede those input errors. With survey_design, "error" raises ValueError for unsupported periods, while "drop" retains the existing NotImplementedError because survey domain estimation is unsupported.

class diff_diff.WooldridgeDiD[source]

Bases: BaseEstimator

Extended Two-Way Fixed Effects (ETWFE) DiD estimator.

Implements the Wooldridge (2025) saturated cohort×time regression (Empirical Economics 69(5), 2545-2587; DOI 10.1007/s00181-025-02807-z) and Wooldridge (2023) nonlinear extensions (logit, Poisson). Produces all four jwdid_estat aggregation types: simple, group, calendar, event. Opt-in surfaces include paper W2025 Section 7 cohort-share aggregation (aggregate(weights="cohort_share"), Eqs. 7.4 + 7.6) and paper W2025 Section 8 heterogeneous cohort-specific linear trends (cohort_trends=True, Eq. 8.1; OLS path only).

Parameters:

  • method ({"ols", "logit", "poisson"}) – Estimation method. "ols" is the linear baseline — valid for any response (Wooldridge 2023) and the usual choice for continuous outcomes; "logit" for binary or fractional outcomes; "poisson" for count data. When method="ols" is used on a binary ({0, 1}) or non-negative integer-count outcome, a UserWarning notes that a matching nonlinear model (logit / Poisson) is often the more appropriate specification — it imposes parallel trends on the link scale rather than in levels, and Wooldridge’s (2023) simulations show the linear model both biased and less precise for such outcomes when the nonlinear mean holds. It rests on a different identifying assumption than linear OLS, so it is a recommended comparison, not an automatic switch; suppress via warnings.filterwarnings.

  • 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” restricts the comparison pool to never-treated units ON THE OLS PATH ONLY, where every (g, t) cell except each cohort’s reference is emitted, so treated units’ pre-treatment rows sit in their own indicators rather than the baseline. On the nonlinear paths (method="logit" / "poisson") only post-treatment cells are emitted – including all cells would make each cohort dummy collinear with the sum of its own indicators – so treated units’ pre-treatment rows ARE part of the identifying comparison there, exactly as under "not_yet_treated". This asymmetry is pre-existing and structural; see the REGISTRY note. Note that n_control_units counts never-treated UNITS on this setting regardless of method, so on the nonlinear paths it under-reports the rows actually doing the comparison (tracked in TODO.md).

  • anticipation (int) – Number of periods before treatment onset to include as treatment cells (anticipation effects). 0 means no anticipation. Must be a non-negative integer; bool is rejected.

  • 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.

  • vcov_type ({"classical", "hc1", "hc2", "hc2_bm", "conley"}, default "hc1") –

    Variance-covariance family for the analytical sandwich, OLS path only. hc1 (default) preserves the prior bit-equal CR1 Liang-Zeger cluster-robust behavior via the within-transform path. hc2_bm auto-routes to a full-dummy saturated design (intercept + treatment cells + unit dummies + time dummies) — FWL preserves cohort coefficients but NOT the hat matrix, so HC2 leverage and Bell-McCaffrey Satterthwaite DOF must be computed on the full FE projection (matches clubSandwich::vcovCR(lm(...), type="CR2") + coef_test()$df_Satt). classical / hc2 are supported via the same full-dummy route AND an auto-drop of the unit auto-cluster (one-way families don’t compose with cluster_ids per the linalg validator). Explicit cluster="X" + one-way vcov_type raises at the validator. "conley" (Conley 1999 spatial-HAC) threads the conley_* params through solve_ols on the within-transform design (conley_lag_cutoff=0 = within-period spatial only; >0 adds within-unit Bartlett serial — the panel-aware path, not pooled cross-sectional, since conley_time / conley_unit are always supplied); the unit auto-cluster is dropped (an explicit cluster= enables the spatial+cluster product kernel) and survey_design= / weights / n_bootstrap>0 are rejected. Conley is OLS-path-only; it routes through the full-dummy design when cohort_trends=True (same as the other full-dummy families), and its vcov flows through aggregate("group"|"calendar"|"event").

    method in {"logit","poisson"} + vcov_type != "hc1" is REJECTED at __init__: the GLM QMLE sandwich path uses pseudo- residuals, and CR2-BM composition with QMLE on canonical-link pseudo- residuals needs derivation + R parity (tracked in DEFERRED.md). Survey designs combined with vcov_type != "hc1" raise NotImplementedError at fit() because the survey TSL / replicate- refit variance overrides the analytical sandwich.

  • cohort_trends (bool, default False) – When True, adds linear dg_i · t cohort-specific trend interactions to the design matrix per paper W2025 Section 8 / Eq. 8.1. Under a heterogeneous-trends DGP this recovers τ even when parallel trends fails (paper Section 8.3). OLS-path only: cohort_trends=True + method ∈ {"logit","poisson"} raises NotImplementedError at __init__. Auto-routes to the full-dummy design regardless of vcov_type (matching the absorb→fixed_effects auto-route). Each cohort that RECEIVES a trend column must have ≥ 2 observed pre-periods in the final analysis sample for dg_i · t to be separately identified from cohort + time FE; fit() raises ValueError otherwise. The check runs after comparison-support filtering and unidentified-cohort exclusion, and skips the last cohort on all-eventually-treated panels because that cohort gets no trend column. On such panels the last cohort’s trend column is dropped per paper Section 5.4, matching the cell-level normalization applied to the design, so cohort_trend_coefs carries G-1 entries. cohort_trends=True + survey_design raises NotImplementedError at fit() (deferred follow-up). cohort_trends=True + control_group="never_treated" also raises NotImplementedError at fit(). The OLS + never_treated branch emits the (g, t) placebo cell dummies (paper Section 4.4 placebo coverage) minus each cohort’s reference cell, and the appended dg_i · t trend columns are still spanned — jointly by those cells and the unit fixed effects, which absorb 1{cohort=g} and so recover the omitted reference. The Section 8 trend specification is therefore unidentified on this branch. Use control_group="not_yet_treated" (the default) for the cohort_trends surface.

  • df_convention ({"residual", "cluster", "normal"}, default "residual") – Degrees-of-freedom convention for the OLS analytical t/p/CI (per-cell and aggregated). "residual" (default) uses the fitted residual df — the 3.9 fix: the default hc1 arms previously used silent normal-theory z; classical/hc2 keep their historical n − rank(X) values bit-for-bit; "cluster" uses the Stata/fixest cluster df G − 1 on hc1-clustered fits (inert on one-way and conley families); "normal" deliberately uses normal-theory z at the fallback level. Survey design df and hc2_bm Bell-McCaffrey DOF always take precedence; the logit/poisson arms are knob-independent (survey df or normal theory — an explicitly non-default value warns at fit time). The default flips to "cluster" at v4.

  • unsupported_period_action ({"drop", "error"}, default "drop") – How to handle periods lacking the required comparison support. "drop" removes those periods before estimation and warns; "error" raises ValueError before removing them. Support requires a positive-weight never-treated observation on OLS with control_group="never_treated"; other paths also admit observations before g - anticipation. This policy is independent of rank_deficient_action and does not control unidentified-cohort exclusion. With survey_design, "drop" still raises NotImplementedError if periods would be removed, because survey domain estimation is not supported; "error" raises ValueError after the existing pre-filter configuration, cohort, and survey-design checks. Later validation (including covariate columns, nonlinear outcomes, and some explicit cluster columns) is not preflighted: an unsupported-period refusal can precede those input errors.

Methods

fit(data, outcome, unit, time[, ...])

Fit the ETWFE model.

get_params([deep])

Get estimator parameters (sklearn-compatible).

set_params(**params)

Set estimator parameters (sklearn-compatible, transactional).
__init__(method='ols', control_group='not_yet_treated', anticipation=0, demean_covariates=True, alpha=0.05, cluster=None, n_bootstrap=0, bootstrap_weights='rademacher', seed=None, rank_deficient_action='warn', vcov_type='hc1', cohort_trends=False, conley_coords=None, conley_cutoff_km=None, conley_metric='haversine', conley_kernel='bartlett', conley_lag_cutoff=None, df_convention='residual', unsupported_period_action='drop')[source]
Parameters:

  • method (str)

  • control_group (str)

  • anticipation (int)

  • demean_covariates (bool)

  • alpha (float)

  • cluster (str | None)

  • n_bootstrap (int)

  • bootstrap_weights (str)

  • seed (int | None)

  • rank_deficient_action (str)

  • vcov_type (str)

  • cohort_trends (bool)

  • conley_coords (Tuple[str, str] | None)

  • conley_cutoff_km (float | None)

  • conley_metric (str)

  • conley_kernel (str)

  • conley_lag_cutoff (int | None)

  • df_convention (str)

  • unsupported_period_action (str)

Return type:

None

property results_: WooldridgeDiDResults
fit(data, outcome, unit, time, first_treat=<not supplied>, exovar=None, xtvar=None, xgvar=None, survey_design=None, cohort=<not supplied>)[source]

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)

  • first_treat (first treatment period column (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)

  • survey_design (SurveyDesign, optional) – Survey design specification for complex survey data. Supports stratified, clustered, and weighted designs via Taylor Series Linearization (TSL). Replicate-weight designs raise NotImplementedError.

  • cohort (str, optional) – Deprecated alias for first_treat (row M-032); warns with FutureWarning and will be removed in 4.0.

Return type:

WooldridgeDiDResults

get_params(deep=True)

Get estimator parameters (sklearn-compatible).

Parameters:

deep (bool, default True) – Accepted for sklearn compatibility (sklearn.base.clone calls get_params(deep=False)) and ignored: no diff-diff estimator nests another estimator.

Returns:

Estimator parameters suitable for passing to __init__. Keys follow the __init__ signature order. Where a class stores a parameter’s raw value under a different attribute (_PARAM_ATTR_ALIASES), the raw value is returned so that type(est)(**est.get_params()) reconstructs the same configuration.

Return type:

Dict[str, Any]

set_params(**params)

Set estimator parameters (sklearn-compatible, transactional).

Validates the merged configuration by constructing a throwaway instance of type(self) - so a rejected call raises the same error __init__ would and leaves this estimator unchanged - then adopts the probe’s parameter attributes and the class’s declared derived-config attributes. Fitted state is never touched.

Parameters:

  • **params (Any) – Estimator parameters. Unknown names raise ValueError before any validation or mutation.

  • self (TSelf)

  • **params

Return type:

self

WooldridgeDiDResults#

Results container returned by WooldridgeDiD.fit().

unsupported_period_action records the fit-time policy and is included in to_dict() and summary(). It remains unchanged by post-fit aggregation or subsequent estimator reconfiguration.

cohort_trend_coefs (populated under cohort_trends=True, OLS path only): Dict[g → δ_g] keyed by treated cohort. The reported slopes are relative to the baseline trend absorbed by the design — the never-treated cohort’s trend (when a never-treated cohort exists) OR the last cohort’s trend (when no never-treated cohort exists, per paper W2025 Section 5.4’s all-eventually-treated drop rule). On all-treated panels the last cohort is intentionally absent from the dict; its slope is the baseline (zero in deviation form).

Note

With the default unsupported_period_action="drop", all-eventually-treated panels estimate. The paper’s Section 5.4 rule is applied to the cohort × time cells as well as the trend columns: periods where no unit is untreated carry no identified ATT(g, t), so they are removed from the estimation sample before the solve and the last cohort becomes the reference. The reduction is always reported — the number of observations and periods dropped, the reason, and any cohort left without cells. Stata’s jwdid performs the same reduction silently, reporting only a smaller N.

See docs/methodology/REGISTRY.md → ## WooldridgeDiD (ETWFE) → “Heterogeneous cohort trends” for the full normalization contract.

class diff_diff.wooldridge_results.WooldridgeDiDResults[source]

Bases: BaseResults

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, weights=...) to compute any of the four jwdid_estat aggregation types under either the default cell-count weighting (weights="cell", matches Stata jwdid_estat) or the paper W2025 opt-in cohort-share weighting (weights="cohort_share", Eqs. 7.4 / 7.6; restricted to type ∈ {"simple", "event"}). cohort_trend_coefs carries Section 8 / Eq. 8.1 estimated δ_g slopes when the fit was produced under WooldridgeDiD(cohort_trends=True). aggregation_weights is keyed by aggregation type and records the active weighting scheme that wrote to each cached surface (surfaced in summary() / to_dataframe() / __repr__). unsupported_period_action records the fit’s policy for periods lacking comparison support ("drop" or "error").

Methods

aggregate(type[, weights])

Compute and store one of the four jwdid_estat aggregation types.

summary([aggregation, alpha])

Print formatted summary table.
group_time_effects: Dict[Tuple[Any, Any], Dict[str, Any]]

key=(g,t), value={att, se, t_stat, p_value, conf_int}

overall_att: float
overall_se: float
overall_t_stat: float
overall_p_value: float
overall_conf_int: Tuple[float, float]
group_effects: Dict[Any, Dict] | None = None
calendar_effects: Dict[Any, Dict] | None = None
event_study_effects: Dict[int, Dict] | None = None
method: str = 'ols'
control_group: str = 'not_yet_treated'
groups: List[Any]
time_periods: List[Any]
n_obs: int = 0
n_treated_units: int = 0
n_control_units: int = 0
alpha: float = 0.05
anticipation: int = 0
survey_metadata: Any | None = None
vcov_type: str = 'hc1'
cluster_name: str | None = None
n_clusters: int | None = None
conley_lag_cutoff: int | None = None
cohort_trend_coefs: Dict[Any, float]
cohort_trends: bool = False
aggregation_weights: Dict[str, str]
df_convention: str | None = None

The estimator’s df_convention configuration echoed onto the results (“residual” | “cluster” | “normal”; added 3.9). Governs the OLS analytical arms only; GLM inference is knob-independent.

unsupported_period_action: str = 'drop'

Fit-time comparison-support period policy ("drop" or "error"). Appended last to preserve generated constructor positional indexes. "error" fits only when no period needs comparison-support filtering; other identification guards, including cohort exclusion, still apply.

__setstate__(state)[source]

Restore pickled state, migrating renamed/added stored fields.

Unpickling bypasses __init__/__post_init__ (the BaseResults pickle-migration contract; SyntheticDiDResults precedent). A pickle written before 3.9 stores _df_one_way (classical/hc2-only residual df) instead of _df_analytic_fallback — carry the old value over so post-fit aggregate() on a legacy result reproduces its fit-time classical/hc2 inference (legacy hc1 pickles carried None there, which restores the pre-3.9 normal-theory aggregation for them). df_convention (added 3.9) defaults to "residual". Missing unsupported_period_action defaults to "drop".

Parameters:

state (Dict[str, Any])

Return type:

None

__init__(group_time_effects, overall_att, overall_se, overall_t_stat, overall_p_value, overall_conf_int, group_effects=None, calendar_effects=None, event_study_effects=None, method='ols', control_group='not_yet_treated', groups=<factory>, time_periods=<factory>, n_obs=0, n_treated_units=0, n_control_units=0, alpha=0.05, anticipation=0, survey_metadata=None, vcov_type='hc1', cluster_name=None, n_clusters=None, conley_lag_cutoff=None, cohort_trend_coefs=<factory>, _bootstrap_used=False, cohort_trends=False, aggregation_weights=<factory>, _gt_weights=<factory>, _n_g_per_cohort=<factory>, _cohort_units_dropped=<factory>, _gt_vcov=None, _gt_keys=<factory>, _df_survey=None, _bm_per_cell_dof=<factory>, _bm_artifacts=None, _df_analytic_fallback=None, df_convention=None, unsupported_period_action='drop')
Parameters:
Return type:

None

aggregate(type, weights='cell')[source]

Compute and store one of the four jwdid_estat aggregation types.

Parameters:

  • type ("simple" | "group" | "calendar" | "event_study") – (“event” is a deprecated spelling of “event_study”; warns with FutureWarning and is rejected in 4.0 - row M-086.)

  • weights ("cell" | "cohort_share", default "cell") – Aggregation weighting scheme. "cell" (default) uses cell- count n_{g,t} observation counts and matches Stata jwdid_estat. "cohort_share" uses paper W2025 Eq. 7.4 ω̂_g = N_g / Σ_{g'} N_{g'} M_{g'} for type="simple" and Eq. 7.6 ω̂_{ge} = N_g / Σ_{g': g'+e ≤ T} N_{g'} for type="event_study". Both formulas reduce to N_g-proportional per-cell weights with the appropriate normalization. The two schemes coincide on balanced panels with uniform within-cohort cell counts (paper Section 7.5). The cohort-share scheme is supported only for type="simple" and type="event_study"; the paper provides no explicit cohort-share formula for "group" or "calendar" aggregations and the library raises ValueError to preserve a fail-closed contract.

  • chaining. (Returns self for)

Return type:

WooldridgeDiDResults

Notes

When vcov_type == "hc2_bm", aggregated inference (t_stat / p_value / conf_int) uses Bell-McCaffrey Satterthwaite contrast-specific DOFs rather than the survey/None default. The BM DOFs are computed lazily from _bm_artifacts via _compute_cr2_bm_contrast_dof and fail-closed (NaN inference) when the helper raises or returns NaN — per feedback_bm_contrast_dof_fail_closed. The contrast column is rebuilt under the active weights scheme so the BM DOF reflects the actual weighting used by ATT + SE.

summary(aggregation=<not supplied>, *, alpha=None)[source]

Print formatted summary table.

Parameters:

  • aggregation (str, optional) – DEPRECATED (row M-087; retired in 4.0): which aggregation to display (“simple”, “group”, “calendar”, “event_study”). From 4.0 summary() takes the library-uniform alpha-only signature and renders the headline simple row; select other aggregations via aggregate(...) / to_dataframe(level=).

  • alpha (float, keyword-only, optional) – Accepted for signature uniformity (spec section 5). The stored confidence columns were computed at fit time; passing a value different from the stored alpha raises rather than silently recomputing or mislabeling - refit at the desired level instead. Keyword-only during the 3.9 shim window because aggregation still holds position 1; it becomes the sole positional parameter in 4.0, matching every other results class.

Return type:

str

to_dict()[source]

Convert headline results to a dictionary.

Returns:

Canonical inference row plus scalar metadata. Detailed group-time / aggregated tables are available via to_dataframe(level=...).

Return type:

Dict[str, Any]

to_dataframe(level=<not supplied>, aggregation=<not supplied>)[source]

Export aggregated effects to a DataFrame.

Parameters:

  • level ("simple" | "group" | "calendar" | "event_study" | "gt") – Use “gt” to export raw group-time effects. (“event” is a deprecated spelling of “event_study”; warns and is rejected in 4.0 - row M-086.)

  • aggregation (str, optional) – Deprecated alias for level (row M-044); warns with FutureWarning and will be removed in 4.0.

Return type:

DataFrame

plot_event_study(weights='cell', **kwargs)[source]

Event study plot. Always calls aggregate('event_study', weights=weights).

Parameters:

  • weights ("cell" | "cohort_share", default "cell") – Aggregation weighting scheme threaded into the underlying aggregate("event_study", ...) call. "cohort_share" produces paper W2025 Eq. 7.6 cohort-share-by-exposure weights (post-treatment k >= 0 only); inference fields are fail-closed to NaN per the Section 7.5 conditional-on-shares contract documented in REGISTRY, and the plot suppresses error bars / CI bands to honor the fail-closed contract (the conditional-on-shares SE would build a misleading normal-theory CI in the plotter).

  • **kwargs – Forwarded to diff_diff.visualization.plot_event_study.

Return type:

None

Notes

The wrapper unconditionally re-aggregates the event study under the requested weights scheme. This avoids the stale-cache hazard where a prior plot_event_study(weights="cohort_share") call would leave the cached event_study_effects restricted to k >= 0 (per the Eq. 7.6 scope), and a subsequent plot_event_study() (default weights="cell") call would silently reuse the cohort-share-keyed cache instead of restoring the full event range including pre-period placebo leads.

property att: float
property se: float
property conf_int: Tuple[float, float]
property p_value: float
property t_stat: float

Example Usage#

Basic OLS (follows 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', first_treat='first_treat')

r.aggregate('event_study').aggregate('group').aggregate('simple')
print(r.to_dataframe(level='event_study'))
print(r.to_dataframe(level='group'))
print(r.summary())

Note

When method="ols" is applied to a binary ({0, 1}) or non-negative integer-count outcome, fit() emits a UserWarning noting that a matching nonlinear model (method="logit" / method="poisson") is often the more appropriate specification for such outcomes — it imposes parallel trends on the link/index scale rather than in levels (Wooldridge 2023 notes level-PT is only valid for continuous/unbounded outcomes), and in that paper’s simulations the linear model is both biased and less precise where the nonlinear mean holds. It rests on a different identifying assumption than linear OLS, so treat it as a recommended comparison, not an automatic switch. OLS remains a valid QMLE for any response (Wooldridge 2023); suppress the hint via warnings.filterwarnings. The check is heuristic: bounded discrete (binomial-style) outcomes with a known upper bound are not separately detected from unbounded counts.

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 (follows 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', first_treat='first_treat')
r_pois.aggregate('event_study').aggregate('group').aggregate('simple')
print(r_pois.summary())

Logit for binary outcomes (follows Stata jwdid y, method(logit)):

m_logit = WooldridgeDiD(method='logit')
r_logit = m_logit.fit(df, outcome='hi_emp', unit='countyreal',
                      time='year', first_treat='first_treat')
r_logit.aggregate('group').aggregate('simple')
print(r_logit.to_dataframe(level='group'))

Aggregation Methods#

Call .aggregate(type, weights=...), then export with .to_dataframe(level=...) (summary() prints the headline simple aggregation):

Type

Description

Stata equivalent

'event_study'

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

Weighting schemes (weights="cell" default, weights="cohort_share" opt-in):

  • weights="cell" (default) — cell-count n_{g,t} weighting; matches Stata jwdid_estat. Supported for all four aggregation types.

  • weights="cohort_share" — paper W2025 Eq. 7.4 (simple) and Eq. 7.6 (event, restricted to k >= 0) cohort-share weighting. Supported only for type="simple" and type="event_study"; raises on type ∈ {"group","calendar"} (no paper closed-form). Inference fields (t-stat / p-value / conf-int) are fail-closed to NaN with a UserWarning documenting the conditional-on-shares limitation (paper W2025 Section 7.5). Raises on survey_design is not None (design-consistent cohort totals pending follow-up).

Comparison with Other Staggered Estimators#

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