Standard errors for two-way clustering with serially correlated time effects

TL;DR

This paper develops improved standard errors and an asymptotic theory for two-way clustered panels allowing for serial dependence in time effects, enabling more accurate inference in complex panel data models.

Contribution

It introduces a novel variance estimator and distribution theory that accommodate serially correlated time effects, extending beyond existing two-way clustering methods.

Findings

Proposed estimator is consistent and asymptotically normal.

Simulation shows improved confidence interval coverage.

Application demonstrates practical relevance in finance models.

Abstract

We propose improved standard errors and an asymptotic distribution theory for two-way clustered panels. Our proposed estimator and theory allow for arbitrary serial dependence in the common time effects, which is excluded by existing two-way methods, including the popular two-way cluster standard errors of Cameron, Gelbach, and Miller (2011) and the cluster bootstrap of Menzel (2021). Our asymptotic distribution theory is the first which allows for this level of inter-dependence among the observations. Under weak regularity conditions, we demonstrate that the least squares estimator is asymptotically normal, our proposed variance estimator is consistent, and t-ratios are asymptotically standard normal, permitting conventional inference. We present simulation evidence that confidence intervals constructed with our proposed standard errors obtain superior coverage performance relative to existing methods. We illustrate the relevance of the proposed method in an empirical application to a standard Fama-French three-factor regression.