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Inference about clustering and parametric assumptions in covariance matrix estimation

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  • Packalen, Mikko
  • Wirjanto, Tony S.

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.

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Bibliographic Info

Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

Volume (Year): 56 (2012)
Issue (Month): 1 (January)
Pages: 1-14

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Handle: RePEc:eee:csdana:v:56:y:2012:i:1:p:1-14

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Keywords: Covariance matrix estimator Cluster-robust Heteroskedasticity-robust Power Size; finite samples;

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  1. Joseph G. Altonji & Lewis M. Segal, 1994. "Small sample bias in GMM estimation of covariance structures," Working Paper Series, Macroeconomic Issues 94-8, Federal Reserve Bank of Chicago.
  2. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, Econometric Society, vol. 48(4), pages 817-38, May.
  3. 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..
  4. 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, Center for Policy Research, Maxwell School, Syracuse University 111, Center for Policy Research, Maxwell School, Syracuse University.
  5. James G. MacKinnon & Halbert White, 1983. "Some Heteroskedasticity Consistent Covariance Matrix Estimators with Improved Finite Sample Properties," Working Papers, Queen's University, Department of Economics 537, Queen's University, Department of Economics.
  6. Gabriel Montes-Rojas & Walter Sosa-Escudero, 2010. "Robust tests for heteroskedasticity in the one-way error components model," Post-Print hal-00768191, HAL.
  7. Gabor Kezdi, 2005. "Robus Standard Error Estimation in Fixed-Effects Panel Models," Econometrics, EconWPA 0508018, EconWPA.
  8. 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.
  9. Marianne Bertrand & Esther Duflo & Sendhil Mullainathan, 2002. "How Much Should We Trust Differences-in-Differences Estimates?," NBER Working Papers 8841, National Bureau of Economic Research, Inc.
  10. James H. Stock & Mark W. Watson, 2006. "Heteroskedasticity-Robust Standard Errors for Fixed Effects Panel Data Regression," NBER Technical Working Papers 0323, National Bureau of Economic Research, Inc.
  11. Hansen, Christian B., 2007. "Asymptotic properties of a robust variance matrix estimator for panel data when T is large," Journal of Econometrics, Elsevier, Elsevier, vol. 141(2), pages 597-620, December.
  12. Wooldridge, Jeffrey M., 1991. "On the application of robust, regression- based diagnostics to models of conditional means and conditional variances," Journal of Econometrics, Elsevier, Elsevier, vol. 47(1), pages 5-46, January.
  13. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, Econometric Society, vol. 50(1), pages 1-25, January.
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