diff_diff.compute_pretrends_power

diff_diff.compute_pretrends_power(results, M=None, alpha=0.05, target_power=0.8, violation_type='linear', pre_periods=None)[source]

Convenience function for pre-trends power analysis.

Parameters:

  • results (results object) – Event study results.

  • M (float, optional) – Violation magnitude to evaluate.

  • alpha (float, default=0.05) – Significance level.

  • target_power (float, default=0.80) – Target power for MDV calculation.

  • violation_type (str, default='linear') – Type of violation pattern.

  • pre_periods (list of int, optional) – Explicit list of pre-treatment periods. If None, attempts to infer from results. Use when you’ve estimated all periods as post_periods.

Returns:

Power analysis results.

Return type:

PreTrendsPowerResults

Examples

>>> from diff_diff import MultiPeriodDiD
>>> from diff_diff.pretrends import compute_pretrends_power
>>>
>>> results = MultiPeriodDiD().fit(data, ...)
>>> power_results = compute_pretrends_power(results, pre_periods=[0, 1, 2, 3])
>>> print(f"MDV: {power_results.mdv:.3f}")
>>> print(f"Power: {power_results.power:.1%}")