Affiliation:
1. Department of Economics Queen's University Kingston Ontario Canada
2. Department of Economics and Business Economics Aarhus University Aarhus Denmark
3. Department of Economics Carleton University Ottawa Ontario Canada
Abstract
SummaryWe provide computationally attractive methods to obtain jackknife‐based cluster‐robust variance matrix estimators (CRVEs) for linear regression models estimated by least squares. We also propose several new variants of the wild cluster bootstrap, which involve these CRVEs, jackknife‐based bootstrap data‐generating processes, or both. Extensive simulation experiments suggest that the new methods can provide much more reliable inferences than existing ones in cases where the latter are not trustworthy, such as when the number of clusters is small and/or cluster sizes vary substantially. Three empirical examples illustrate the new methods.
Subject
Economics and Econometrics,Social Sciences (miscellaneous)
Reference41 articles.
1. Political Turnover, Bureaucratic Turnover, and the Quality of Public Services
2. Bias reduction in standard errors for linear regression with multi‐stage samples;Bell R. M.;Survey Methodology,2002
3. Inference with dependent data using cluster covariance estimators
4. Inference with difference‐in‐differences revisited;Brewer M.;Journal of Econometric Methods,2018
5. Bootstrap-Based Improvements for Inference with Clustered Errors
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献