moderndid.wboot_std_ipw_panel

moderndid.wboot_std_ipw_panel(delta_y, d, x, i_weights, n_bootstrap=1000, trim_level=0.995, random_state=None)

Compute bootstrap estimates for standardized IPW DiD with panel data.

Implements a bootstrapped standardized Inverse Probability Weighting (IPW) difference-in-differences estimator for panel data. This estimator uses standardized weights to compute separate means for treated and control groups.

Parameters:

delta_ynumpy.ndarray

A 1D array representing the difference in outcomes between the post-treatment and pre-treatment periods (Y_post - Y_pre) for each unit.

dnumpy.ndarray

A 1D array representing the treatment indicator (1 for treated, 0 for control) for each unit.

xnumpy.ndarray

A 2D array of covariates (including intercept if desired) with shape (n_units, n_features).

i_weightsnumpy.ndarray

A 1D array of individual observation weights for each unit.

n_bootstrapint

Number of bootstrap iterations. Default is 1000.

trim_levelfloat

Maximum propensity score value for control units to avoid extreme weights. Default is 0.995.

random_stateint, RandomState instance or None

Controls the random number generation for reproducibility.

Returns:

numpy.ndarray

A 1D array of bootstrap ATT estimates with length n_bootstrap.

See also

wboot_ipw_panel

Non-standardized IPW bootstrap for panel data.

calculate_pscore_ipt

IPT propensity score estimation.