Cluster-robust inference: A guide to empirical practice

By James G. MacKinnon, Morten Ørregaard Nielsen, Matthew D. Webb. Journal of Econometrics, 2022

281 citations recorded by the source corpus.

Methods for cluster-robust inference are routinely used in economics and many other disciplines. However, it is only recently that theoretical foundations for the use of these methods in many empirically relevant situations have been developed. In this paper, we use these theoretical results to provide a guide to empirical practice. We do not attempt to present a comprehensive survey of the (very large) literature. Instead, we bridge theory and practice by providing a thorough guide on what to do and why, based on recently available econometric theory and simulation evidence. To practice what we preach, we include an empirical analysis of the eﬀects of the minimum wage on labor supply of teenagers using individual data.

Subfields: Economics and Econometrics, Sociology and Political Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4b73f9f1d798b4108b342ecf7aa0f1b98d1a9904

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Sociology and Political Science

In Statistics and Probability

Citations in the corpus

When Should You Adjust Standard Errors for Clustering? (2017, methodology): “It differs from earlier bootstrap variance estimators for clustered settings [e.g., Cameron and Miller, 2015, Menzel, 2021] in that it allows for the possibility that a large fraction of clusters are observed.”