Inference about Clustering and Parametric Assumptions in Covariance Matrix Estimation

Computational Statistics and Data Analysis 56 (2012) 1–14.

Posted: 10 Dec 2010 Last revised: 29 Mar 2013

See all articles by Mikko Packalen

Mikko Packalen

University of Waterloo - Department of Economics

Tony S. Wirjanto

University of Waterloo - School of Accounting and Finance; University of Waterloo, Department of Statistics & Actuarial Science

Date Written: 2012

Abstract

Selecting an estimator for the covariance matrix of a regression’s parameter estimates is an important step in hypothesis testing. From less to more robust estimators, the choices available to researchers include Eicker/White heteroskedasticity-robust estimator, cluster-robust estimator, and multi-way cluster-robust estimator. The rationale for choosing a less robust covariance matrix estimator is that tests conducted using this estimator can have better power properties. This motivates tests that examine the appropriate level of robustness in covariance matrix estimation. In this paper, we propose a new robustness testing strategy, and show that it can dramatically improve inference about the proper level of robustness in covariance matrix estimation. In an empirically relevant example, namely the placebo treatment application of Bertrand, Duflo and Mullainathan (2004), the power of the proposed robustness testing strategy against the null hypothesis ‘‘no clustering’’ is 0.82 while the power of the existing robustness testing approach against the same null is only 0.04. We also show why the existing clustering test and other applications of the White (1980) robustness testing approach often perform poorly, which to our knowledge has not been well understood. The insight into why this existing testing approach performs poorly is also the basis for the proposed robustness testing strategy.

Keywords: covariance matrix estimator; cluster-robust; heteroskedasticity-robust; power; size, finite samples

JEL Classification: C10, C12, C13, C52

Suggested Citation

Packalen, Mikko and Wirjanto, Tony S., Inference about Clustering and Parametric Assumptions in Covariance Matrix Estimation (2012). Computational Statistics and Data Analysis 56 (2012) 1–14. , Available at SSRN: https://ssrn.com/abstract=1722662 or http://dx.doi.org/10.2139/ssrn.1722662

Mikko Packalen (Contact Author)

University of Waterloo - Department of Economics ( email )

Waterloo, Ontario N2L 3G1
Canada

Tony S. Wirjanto

University of Waterloo - School of Accounting and Finance ( email )

200 University Avenue West
Waterloo, Ontario N2L 3G1
Canada
519-888-4567 x35210 (Phone)

HOME PAGE: http://https://uwaterloo.ca/statistics-and-actuarial-science/people-profiles/tony-wirjanto

University of Waterloo, Department of Statistics & Actuarial Science ( email )

200 University Avenue West
Waterloo, Ontario N2L 3G1
Canada
519-888-4567 x35210 (Phone)
519-746-1875 (Fax)

HOME PAGE: http://math.uwaterloo.ca/statistics-and-actuarial-science/people-profiles/tony-wirjanto

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