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Heteroskedasticity-Robust Inference in Finite Samples

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  • Jerry A. Hausman
  • Christopher J. Palmer

Abstract

Since the advent of heteroskedasticity-robust standard errors, several papers have proposed adjustments to the original White formulation. We replicate earlier findings that each of these adjusted estimators performs quite poorly in finite samples. We propose a class of alternative heteroskedasticity-robust tests of linear hypotheses based on an Edgeworth expansions of the test statistic distribution. Our preferred test outperforms existing methods in both size and power for low, moderate, and severe levels of heteroskedasticity.

Suggested Citation

  • Jerry A. Hausman & Christopher J. Palmer, 2011. "Heteroskedasticity-Robust Inference in Finite Samples," NBER Working Papers 17698, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:17698
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    References listed on IDEAS

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    1. Cribari-Neto, Francisco, 2004. "Asymptotic inference under heteroskedasticity of unknown form," Computational Statistics & Data Analysis, Elsevier, vol. 45(2), pages 215-233, March.
    2. Rothenberg, Thomas J, 1988. "Approximate Power Functions for Some Robust Tests of Regression Coefficients," Econometrica, Econometric Society, vol. 56(5), pages 997-1019, September.
    3. Hausman, Jerry & Kuersteiner, Guido, 2008. "Difference in difference meets generalized least squares: Higher order properties of hypotheses tests," Journal of Econometrics, Elsevier, vol. 144(2), pages 371-391, June.
    4. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    5. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
    6. 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.
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    Cited by:

    1. Guido W. Imbens & Michal Kolesár, 2016. "Robust Standard Errors in Small Samples: Some Practical Advice," The Review of Economics and Statistics, MIT Press, vol. 98(4), pages 701-712, October.
    2. Peter Z. Schochet, 2015. "Statistical Theory for the RCT-YES Software: Design-Based Causal Inference for RCTs," Mathematica Policy Research Reports a0c005c003c242308a92c02dc, Mathematica Policy Research.
    3. Romano, Joseph P. & Wolf, Michael, 2017. "Resurrecting weighted least squares," Journal of Econometrics, Elsevier, vol. 197(1), pages 1-19.
    4. repec:eee:ecolet:v:159:y:2017:i:c:p:28-32 is not listed on IDEAS
    5. Matei Demetrescu & Christoph Hanck, 2013. "Nonlinear IV panel unit root testing under structural breaks in the error variance," Statistical Papers, Springer, vol. 54(4), pages 1043-1066, November.
    6. Abe, Naohito & Ueno, Yuko, 2015. "Measuring Inflation Expectations: Consumers' Heterogeneity and Nonlinearity," RCESR Discussion Paper Series DP15-5, Research Center for Economic and Social Risks, Institute of Economic Research, Hitotsubashi University.

    More about this item

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General

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