diff_diff.generate_event_study_data#

diff_diff.generate_event_study_data(n_units=300, n_pre=5, n_post=5, treatment_fraction=0.5, treatment_effect=5.0, unit_fe_sd=2.0, noise_sd=2.0, seed=None)[source]

Generate synthetic data for event study analysis.

Creates panel data with simultaneous treatment at period n_pre. Useful for testing MultiPeriodDiD, pre-trends power analysis, and HonestDiD sensitivity analysis.

Parameters:

  • n_units (int, default=300) – Total number of units in the panel.

  • n_pre (int, default=5) – Number of pre-treatment periods.

  • n_post (int, default=5) – Number of post-treatment periods.

  • treatment_fraction (float, default=0.5) – Fraction of units that receive treatment.

  • treatment_effect (float, default=5.0) – True average treatment effect on the treated.

  • unit_fe_sd (float, default=2.0) – Standard deviation of unit fixed effects.

  • noise_sd (float, default=2.0) – Standard deviation of idiosyncratic noise.

  • seed (int, optional) – Random seed for reproducibility.

Returns:

Synthetic event study data with columns: - unit: Unit identifier - period: Time period - treated: Binary unit-level treatment indicator - post: Binary post-treatment indicator - outcome: Outcome variable - event_time: Time relative to treatment (negative=pre, 0+=post) - true_effect: The true treatment effect for this observation

Return type:

pd.DataFrame

Examples

Generate event study data:

>>> data = generate_event_study_data(n_units=300, n_pre=5, n_post=5, seed=42)
>>> data['event_time'].unique()
array([-5, -4, -3, -2, -1,  0,  1,  2,  3,  4])

Use with MultiPeriodDiD:

>>> from diff_diff import MultiPeriodDiD
>>> mp_did = MultiPeriodDiD()
>>> results = mp_did.fit(data, outcome='outcome', treatment='treated',
...                      time='period', post_periods=[5, 6, 7, 8, 9])

Notes

The event_time column is relative to treatment: - Negative values: pre-treatment periods - 0: first post-treatment period - Positive values: subsequent post-treatment periods