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Improvement in finite-sample properties of GMM-based Wald tests

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  • Qihui Chen
  • Yu Ren

Abstract

GMM-based Wald tests tend to overreject when used for small samples, mainly due to inaccurate estimation of the weighting matrix. This article proposes applying the shrinkage method to address this problem. Using a possibly-misspecified factor model, the shrinkage method can provide a good estimator for the weighting matrix, and hence improve the finite-sample performance of the GMM-based Wald tests. Copyright Springer-Verlag 2013

Suggested Citation

  • Qihui Chen & Yu Ren, 2013. "Improvement in finite-sample properties of GMM-based Wald tests," Computational Statistics, Springer, vol. 28(2), pages 735-749, April.
    • Handle: RePEc:spr:compst:v:28:y:2013:i:2:p:735-749
    DOI: 10.1007/s00180-012-0326-0
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    References listed on IDEAS

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    1. Chistiano, Lawrence J & den Haan, Wouter J, 1996. "Small-Sample Properties of GMM for Business-Cycle Analysis," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 309-327, July.
    2. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    3. Geert Bekaert & Robert J. Hodrick, 2001. "Expectations Hypotheses Tests," Journal of Finance, American Finance Association, vol. 56(4), pages 1357-1394, August.
    4. Andersen, Torben G & Sorensen, Bent E, 1996. "GMM Estimation of a Stochastic Volatility Model: A Monte Carlo Study," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 328-352, July.
    5. Burnside, Craig & Eichenbaum, Martin S, 1996. "Small-Sample Properties of GMM-Based Wald Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 294-308, July.
    6. Rothenberg, Thomas J., 1984. "Approximating the distributions of econometric estimators and test statistics," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 15, pages 881-935, Elsevier.
    7. Ren, Yu & Shimotsu, Katsumi, 2009. "Improvement in finite sample properties of the Hansen-Jagannathan distance test," Journal of Empirical Finance, Elsevier, vol. 16(3), pages 483-506, June.
    8. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    9. Ledoit, Olivier & Wolf, Michael, 2003. "Improved estimation of the covariance matrix of stock returns with an application to portfolio selection," Journal of Empirical Finance, Elsevier, vol. 10(5), pages 603-621, December.
    10. Hall, Peter & Horowitz, Joel L, 1996. "Bootstrap Critical Values for Tests Based on Generalized-Method-of-Moments Estimators," Econometrica, Econometric Society, vol. 64(4), pages 891-916, July.
    11. Hansen, Lars Peter & Heaton, John & Yaron, Amir, 1996. "Finite-Sample Properties of Some Alternative GMM Estimators," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 262-280, July.
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