Computational Statistics

, Volume 28, Issue 2, pp 735–749

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

Original Paper
  • 200 Downloads

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.

Keywords

Generalized method of moments Wald tests Finite-sample properties Covariance matrix Shrinkage method 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Andersen T, Sørensen B (1996) GMM estimation of a stochastic volatility model: a Monte Carlo study. J Bus Econ Stat 14: 328–352Google Scholar
  2. Bekaert G, Hodrick R (2001) Expectations hypotheses tests. J Finance 56: 1357–1394CrossRefGoogle Scholar
  3. Burnside C, Eichenbaum M (1996) Small-sample properties of GMM-based Wald tests. J Bus Econ Stat 7: 265–296Google Scholar
  4. Christiano L, Den Haan W (1996) Small-sample properties of GMM for business-cycle analysis. J Bus Econ Stat 14: 309–327Google Scholar
  5. Hansen LP (1982) Large sample properties of generalized method of moments estimators. Econometrica 50: 1029–1054MathSciNetMATHCrossRefGoogle Scholar
  6. Hansen L, Heaton J, Yaron A (1996) Finite-sample properties of some alternative GMM estimators. J Bus Econ Stat 14: 262–280Google Scholar
  7. Hall P, Horowitz L (1996) Bootstrap critical values for tests based on generalized-methood-of-moments estimators. Econometrica 64: 891–916MathSciNetMATHCrossRefGoogle Scholar
  8. Kan R, Zhang C (1999) GMM tests of stochastic discount factor models with useless factors. J Financial Econ 54: 103–127CrossRefGoogle Scholar
  9. Ledoit O, Wolf M (2003) Improved estimation of the covariance matrix of stock returns with an application to portfolio selection. J Empir Finance 10: 603–621CrossRefGoogle Scholar
  10. Newey W, West K (1987) A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica 55: 703–708MathSciNetMATHCrossRefGoogle Scholar
  11. Ren Y, Shimotsu K (2009) Improvement in finite sample properties of the Hansen-Jagannathan distance test. J Empir Finance 16: 483–506CrossRefGoogle Scholar
  12. Rothenberg T (1984) Approximating the distributions of econometric estimators and test statistics. Handb Econom 2: 881–935CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.Department of EconomicsUniversity of CaliforniaSan DiegoUSA
  2. 2.WISE, MOE Key Lab of Econometrics and Fujian Key Lab of StatisticsXiamen UniversityFujianChina

Personalised recommendations