Jackknife inference for multiway clustering and CS-DiD in Stata: twowayjack and csdidjack

This presentation introduces two Stata packages: twowayjack and csdidjack, which implement jackknife-based inference for models with clustered data. The twowayjack command provides robust inference for OLS regression models with two-way clustering, such as by unit and time. It implements the CV3 standard errors from MacKinnon, Nielsen, and Webb (2024), which are designed to remain valid even with few clusters in one or both dimensions. These estimators omit one-cluster resampling to account for dependence across both clustering dimensions. The csdidjack command applies these ideas to the csdid estimator of Callaway and Sant'Anna (2021), providing improved inference for average treatment effects on the treated (ATET) in staggered adoption settings. It also supports jackknife-based CV3 inference for calendar-time, cohort-based, and ATET_gt effects. The underlying methodology is described in MacKinnon, Nielsen, Webb, and Karim (2025), which extends jackknife and bootstrap inference to this setting. Both tools offer practical solutions for empirical researchers facing clustered data and limited numbers of clusters. The packages are freely available on GitHub as community-contributed Stata commands: twowayjack and csdidjack.