diff_diff.WooldridgeDiD#

class diff_diff.WooldridgeDiD[source]#

Bases: object

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.

Methods

__init__([method, control_group, ...])

fit(data, outcome, unit, time, cohort[, ...])

Fit the ETWFE model.

get_params()

Return estimator parameters (sklearn-compatible).

set_params(**params)

Set estimator parameters (sklearn-compatible).

Attributes

results_

__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')[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)

Return type:

None

classmethod __new__(*args, **kwargs)#