Inference about clustering and parametric assumptions in covariance matrix estimation
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"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.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
References listed on IDEAS
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.:
- 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.
- 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.
- 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 1093, Princeton University, Department of Economics, Center for Economic Policy Studies..
- 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.
- 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.
- Doug Miller & A. Colin Cameron & Jonah B. Gelbach, 2006. "Bootstrap-Based Improvements for Inference with Clustered Errors," Working Papers 621, University of California, Davis, Department of Economics.
- Marianne Bertrand & Esther Duflo & Sendhil Mullainathan, 2004.
"How Much Should We Trust Differences-In-Differences Estimates?,"
The Quarterly Journal of Economics,
Oxford University Press, vol. 119(1), pages 249-275.
- 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.
- 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.
- Joseph G. Altonji & Lewis M. Segal, 1994.
"Small Sample Bias in GMM Estimation of Covariance Structures,"
NBER Technical Working Papers
0156, National Bureau of Economic Research, Inc.
- Altonji, Joseph G & Segal, Lewis M, 1996. "Small-Sample Bias in GMM Estimation of Covariance Structures," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 353-66, July.
- 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.
- White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
- 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 hal-00768191, HAL.
- 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.
- 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.
- James H. Stock & Mark W. Watson, 2008.
"Heteroskedasticity-Robust Standard Errors for Fixed Effects Panel Data Regression,"
Econometric Society, vol. 76(1), pages 155-174, 01.
- 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.
- 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.
- Gabor Kezdi, 2005. "Robus Standard Error Estimation in Fixed-Effects Panel Models," Econometrics 0508018, EconWPA.
When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:56:y:2012:i:1:p:1-14. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.