Package: NonlinearDiD 0.2.0

NonlinearDiD: Staggered Difference-in-Differences with Nonlinear Outcomes

Supports staggered difference-in-differences designs with nonlinear outcomes for both panel and repeated cross-section data. Implements estimators for staggered treatment adoption with binary, count, and other nonlinear outcomes, extending Callaway and Sant'Anna (2021) <doi:10.1016/j.jeconom.2020.12.001> to settings with nonlinear outcome models such as logit, probit, and Poisson. For panel data, units are followed over time and 'idname' identifies repeated observations. For repeated cross-section data, observations are independent within each time period; 'idname' is optional and may identify survey records or households, but the estimator does not require the same units to appear across periods. Repeated cross-section estimation includes pooled quasi-maximum likelihood approaches motivated by Wooldridge (2023) <doi:10.1093/ectj/utad016>, with optional weighting and clustered inference. Methods also draw on Roth and Sant'Anna (2023) <doi:10.3982/ECTA19402> and Sant'Anna and Zhao (2020) <doi:10.1016/j.jeconom.2020.06.003>.

Authors:Subir Hait [aut, cre]