Fast and reliable jackknife and bootstrap methods for cluster-robust inference

Arbeitspapier

We provide new and computationally attractive methods, based on jackknifing by cluster, to obtain cluster-robust variance matrix estimators (CRVEs) for linear regres- sion models estimated by least squares. These estimators have previously been com- putationally infeasible except for small samples. We also propose several new variants of the wild cluster bootstrap, which involve the new 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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Queen’s Economics Department Working Paper ; No. 1485

Classification and Topics

• Classification: Wirtschaft ; Econometric and Statistical Methods and Methodology: General ; Hypothesis Testing: General ; Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions ; Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models
• Subject: bootstrap ; clustered data ; grouped data ; cluster-robust variance estima-tor ; CRVE ; cluster sizes ; jackknife ; wild cluster bootstrap

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): MacKinnon, James G. ; Nielsen, Morten Ørregaard ; Webb, Matthew
• Event: Veröffentlichung
• (who): Queen's University, Department of Economics
• (where): Kingston (Ontario)
• (when): 2022

Further information

• Handle: http://hdl.handle.net/10419/281089
• Last update: 10.03.2025, 11:44 AM CET