Inference about clustering and parametric assumptions in covariance matrix estimation
AbstractSelecting 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"Â 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 theÂ 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.
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Bibliographic InfoArticle provided by Elsevier in its journal Computational Statistics & Data Analysis.
Volume (Year): 56 (2012)
Issue (Month): 1 (January)
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Web page: http://www.elsevier.com/locate/csda
Covariance matrix estimator Cluster-robust Heteroskedasticity-robust Power Size; finite samples;
Other versions of this item:
- Mikko Packalen & Tony Wirjanto, 2010. "Inference about Clustering and Parametric Assumptions in Covariance Matrix Estimation," Working Papers 1012, University of Waterloo, Department of Economics, revised Nov 2010.
- C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Krueger, Alan B. & Mueller, Andreas I., 2008.
"Job Search and Unemployment Insurance: New Evidence from Time Use Data,"
IZA Discussion Papers
3667, Institute for the Study of Labor (IZA).
- Krueger, Alan B. & Mueller, Andreas, 2010. "Job search and unemployment insurance: New evidence from time use data," Journal of Public Economics, Elsevier, vol. 94(3-4), pages 298-307, April.
- Alan B. Krueger & Andreas Mueller, 2008. "Job Search and Unemployment Insurance: New Evidence from Time Use Data," Working Papers 1070, Princeton University, Department of Economics, Industrial Relations Section..
- Alan B. Krueger & Andreas Mueller, 2008. "Job Search and Unemployment Insurance: New Evidence from Time Use Data," Working Papers 1093, Princeton University, Department of Economics, Center for Economic Policy Studies..
- Doug Miller & A. Colin Cameron & Jonah B. Gelbach, 2006.
"Bootstrap-Based Improvements for Inference with Clustered Errors,"
621, University of California, Davis, Department of Economics.
- A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2008. "Bootstrap-Based Improvements for Inference with Clustered Errors," The Review of Economics and Statistics, MIT Press, vol. 90(3), pages 414-427, August.
- A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2007. "Bootstrap-Based Improvements for Inference with Clustered Errors," NBER Technical Working Papers 0344, National Bureau of Economic Research, Inc.
- Wooldridge, Jeffrey M., 1991. "On the application of robust, regression- based diagnostics to models of conditional means and conditional variances," Journal of Econometrics, Elsevier, vol. 47(1), pages 5-46, January.
- Baltagi, Badi H. & Jung, Byoung Cheol & Song, Seuck Heun, 2010.
"Testing for heteroskedasticity and serial correlation in a random effects panel data model,"
Journal of Econometrics,
Elsevier, vol. 154(2), pages 122-124, February.
- Badi H. Baltagi & Byoung Cheol Jung & Seuck Heun Song, 2008. "Testing for Heteroskedasticity and Serial Correlation in a Random Effects Panel Data Model," Center for Policy Research Working Papers 111, Center for Policy Research, Maxwell School, Syracuse University.
- Montes-Rojas, Gabriel & Sosa-Escudero, Walter, 2011.
"Robust tests for heteroskedasticity in the one-way error components model,"
Journal of Econometrics,
Elsevier, vol. 160(2), pages 300-310, February.
- Gabriel Montes-Rojas & Walter Sosa-Escudero, 2010. "Robust tests for heteroskedasticity in the one-way error components model," Post-Print peer-00768191, HAL.
- Hansen, Christian B., 2007. "Asymptotic properties of a robust variance matrix estimator for panel data when T is large," Journal of Econometrics, Elsevier, vol. 141(2), pages 597-620, December.
- White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
- White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-38, May.
- MacKinnon, James G. & White, Halbert, 1985.
"Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties,"
Journal of Econometrics,
Elsevier, vol. 29(3), pages 305-325, September.
- James G. MacKinnon & Halbert White, 1983. "Some Heteroskedasticity Consistent Covariance Matrix Estimators with Improved Finite Sample Properties," Working Papers 537, Queen's University, Department of Economics.
- Gabor Kezdi, 2005. "Robus Standard Error Estimation in Fixed-Effects Panel Models," Econometrics 0508018, EconWPA.
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