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Inference about Clustering and Parametric Assumptions in Covariance Matrix Estimation

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  • Mikko Packalen

    (Department of Economics, University of Waterloo)

  • Tony Wirjanto

    (School of Accounting & Finance and Department of Statistics and Actuarial Science, University of Waterloo)

Abstract

Selecting an estimator for the variance covariance matrix is an important step in hypothesis testing. From less robust to more robust, the available choices include: Eicker/White heteroskedasticity-robust standard errors, Newey and West heteroskedasticity-and-autocorrelation- robust standard errors, and cluster-robust standard errors. The rationale for using a less robust covariance matrix estimator is that tests conducted using a less robust covariance matrix estimator can have better power properties. This motivates tests that examine the appropriate level of robustness in covariance matrix estimation. 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. Our main focus is on inference about clustering although the proposed robustness testing strategy can also improve inference about parametric assumptions in covariance matrix estimation, which we demonstrate for the case of testing for heteroskedasticity. We also show why the existing clustering test and other applications of the White (1980) robustness testing approach 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

Paper provided by University of Waterloo, Department of Economics in its series Working Papers with number 1012.

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Length: 38 pages
Date of creation: Nov 2010
Date of revision: Nov 2010
Handle: RePEc:wat:wpaper:1012

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  1. Cameron, A. Colin & Miller, Douglas & Gelbach, Jonah B., 2006. "Bootstrap-Based Improvements for Inference with Clustered Errors," Working Papers 06-21, University of California at Davis, Department of Economics.
  2. Gabor Kezdi, 2005. "Robus Standard Error Estimation in Fixed-Effects Panel Models," Econometrics 0508018, EconWPA.
  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. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
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