moderndid.wboot_dr_tr_panel#

moderndid.wboot_dr_tr_panel(delta_y, d, x, i_weights, n_bootstrap=1000, trim_level=0.995, random_state=None)[source]#

Compute bootstrap estimates for traditional doubly-robust DiD with panel data.

Implements a traditional bootstrap approach for doubly-robust difference-in-differences with panel data that uses standard logistic regression for propensity score estimation.

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.