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Small Sample Bias of Alternative Estimation Methods for Moment Condition Models: Monte Carlo Evidence for Covariance Structures

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Author Info
Joaquim Ramalho (Universidade de Evora)

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Abstract

It is now widely recognized that the most commonly used efficient two-step GMM estimator may have large bias in small samples. In this paper we analyze by simulation the finite sample bias of two classes of alternative estimators. The first includes estimators which are asymptotically first-order equivalent to the GMM estimator, namely the continuous-updating, exponential tilting, and empirical likelihood estimators. Analytical and bootstrap bias-adjusted GMM estimators form the second class of alternatives. The Monte Carlo simulation study conducted in the paper for covariance structure models shows that all alternative estimators offer much reduced bias as compared to the GMM estimator, particularly the empirical likelihood and some of the bias-corrected GMM estimators.

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Publisher Info
Article provided by Berkeley Electronic Press in its journal Studies in Nonlinear Dynamics & Econometrics.

Volume (Year): 9 (2005)
Issue (Month): 1 ()
Pages: 1202-1202
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Handle: RePEc:bep:sndecm:9:2005:1:1202-1202

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Related research
Keywords: GMM Generalized Empirical Likelihood Analytical and Bootstrap Bias-Adjusted Estimators Covariance Structures

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  1. Donald, Stephen G & Newey, Whitney K, 2001. "Choosing the Number of Instruments," Econometrica, Econometric Society, vol. 69(5), pages 1161-91, September.
  2. 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 262-80, July.
  3. James H. Stock & Jonathan Wright, 2000. "GMM with Weak Identification," Econometrica, Econometric Society, vol. 68(5), pages 1055-1096, September.
  4. Guido W. Imbens & Richard H. Spady & Phillip Johnson, 1998. "Information Theoretic Approaches to Inference in Moment Condition Models," Econometrica, Econometric Society, vol. 66(2), pages 333-358, March.
    Other versions:
  5. 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.
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  6. Ramalho, Joaquim J. S. & Smith, Richard J., 2002. "Generalized empirical likelihood non-nested tests," Journal of Econometrics, Elsevier, vol. 107(1-2), pages 99-125, March. [Downloadable!] (restricted)
  7. Behrman, Jere R & Rosenzweig, Mark R & Taubman, Paul, 1994. "Endowments and the Allocation of Schooling in the Family and in the Marriage Market: The Twins Experiment," Journal of Political Economy, University of Chicago Press, vol. 102(6), pages 1131-74, December. [Downloadable!] (restricted)
  8. Kocherlakota, Narayana R., 1990. "On tests of representative consumer asset pricing models," Journal of Monetary Economics, Elsevier, vol. 26(2), pages 285-304, October. [Downloadable!] (restricted)
  9. Joel L. Horowitz, 1996. "Bootstrap Methods For Covariance Structures," Econometrics 9610003, EconWPA. [Downloadable!]
  10. Chamberlain, Gary, 1987. "Asymptotic efficiency in estimation with conditional moment restrictions," Journal of Econometrics, Elsevier, vol. 34(3), pages 305-334, March. [Downloadable!] (restricted)
  11. Griliches, Zvi, 1979. "Sibling Models and Data in Economics: Beginnings of a Survey," Journal of Political Economy, University of Chicago Press, vol. 87(5), pages S37-64, October. [Downloadable!] (restricted)
  12. Whitney K. Newey & Joaquim J. S. Ramalho & Richard Smith, 2003. "Asymptotic bias for GMM and GEL estimators with estimated nuisance parameters," CeMMAP working papers CWP05/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies. [Downloadable!]
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