NATIONAL BUREAU OF ECONOMIC RESEARCH
NATIONAL BUREAU OF ECONOMIC RESEARCH

Robust Inference with Multi-way Clustering

A. Colin Cameron, Jonah B. Gelbach, Douglas L. Miller

NBER Technical Working Paper No. 327
Issued in September 2006
NBER Program(s):   TWP

In this paper we propose a new variance estimator for OLS as well as for nonlinear estimators such as logit, probit and GMM, that provcides cluster-robust inference when there is two-way or multi-way clustering that is non-nested. The variance estimator extends the standard cluster-robust variance estimator or sandwich estimator for one-way clustering (e.g. Liang and Zeger (1986), Arellano (1987)) and relies on similar relatively weak distributional assumptions. Our method is easily implemented in statistical packages, such as Stata and SAS, that already offer cluster-robust standard errors when there is one-way clustering. The method is demonstrated by a Monte Carlo analysis for a two-way random effects model; a Monte Carlo analysis of a placebo law that extends the state-year effects example of Bertrand et al. (2004) to two dimensions; and by application to two studies in the empirical public/labor literature where two-way clustering is present.

download in pdf format
   (365 K)

email paper

This paper is available as PDF (365 K) or via email.

Machine-readable bibliographic record - MARC, RIS, BibTeX

Document Object Identifier (DOI): 10.3386/t0327

Users who downloaded this paper also downloaded these:
Bertrand, Duflo, and Mullainathan w8841 How Much Should We Trust Differences-in-Differences Estimates?
Cameron, Gelbach, and Miller t0344 Bootstrap-Based Improvements for Inference with Clustered Errors
Petersen w11280 Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches
Stock and Watson t0323 Heteroskedasticity-Robust Standard Errors for Fixed Effects Panel Data Regression
Dornbusch and Fischer w0327 Sterling and the External Balance
 
Publications
Activities
Meetings
NBER Videos
Data
People
About

Support
National Bureau of Economic Research, 1050 Massachusetts Ave., Cambridge, MA 02138; 617-868-3900; email: info@nber.org

Contact Us