moderndid.wboot_reg_rc#

moderndid.wboot_reg_rc(y, post, d, x, i_weights, n_bootstrap=1000, random_state=None)[source]#

Compute bootstrap estimates for regression-based robust DiD with repeated cross-sections.

Implements a regression-based difference-in-differences estimator that uses outcome regression on the control group only, without propensity scores. It is designed for settings with 2 time periods and 2 groups. The estimator fits separate regressions for pre and post periods using control units only, then computes the ATT using these predictions.

Parameters:

ynumpy.ndarray: A 1D array representing the outcome variable for each unit.
postnumpy.ndarray: A 1D array representing the post-treatment period indicator (1 for post, 0 for 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.
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.