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GENERALIZED EMPIRICAL LIKELIHOOD–BASED MODEL SELECTION CRITERIA FOR MOMENT CONDITION MODELS

Published online by Cambridge University Press:  24 September 2003

Han Hong ,
Bruce Preston  and
Matthew Shum
Han Hong
Affiliation:
Princeton University
Bruce Preston
Affiliation:
Princeton University
Matthew Shum
Affiliation:
Johns Hopkins University
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Abstract

This paper proposes model selection criteria (MSC) for unconditional moment models using generalized empirical likelihood (GEL) statistics. The use of GEL-statistics in lieu of J-statistics (in the spirit of Andrews, 1999, Econometrica 67, 543–564; and Andrews and Lu, 2001, Journal of Econometrics 101, 123–164) leads to an alternative interpretation of the MSCs that emphasizes the common information-theoretic rationale underlying model selection procedures for both parametric and semiparametric models. The result of this paper also provides a GEL-based model selection alternative to the information criteria–based nonnested tests for generalized method of moments models considered in Kitamura (2000, University of Wisconsin). The results of a Monte Carlo experiment are reported to illustrate the finite-sample performance of the selection criteria and their impact on parameter estimation.The authors gratefully acknowledge support from the NSF (Hong: SES-0079495, Shum: SES-0003352) and the Fellowship of Woodrow Wilson Scholars (Preston). We thank the co-editor Don Andrews, Xiaohong Chen, John Geweke, Bo Honore, Yuichi Kitamura, Serena Ng, Harry Paarsch, Gautam Tripathi, and two anonymous referees for insightful suggestions and helpful comments.

Type
Research Article
Information
Econometric Theory , Volume 19 , Issue 6 , December 2003 , pp. 923 - 943
Copyright
© 2003 Cambridge University Press

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References

REFERENCES

Ahn, H., Y. Kitamura, & G. Tripathi (2001) Empirical Likelihood-Based Inference in Conditional Moment Restriction Models. Working paper, Department of Economics, University of Wisconsin.
Amemiya, T. (1985) Advanced Econometrics. Cambridge: Harvard University Press.
Andrews, D.W. (1991) Heteroskedasticity and autocorrelation consistent covariance matrix estimation. Econometrica 59, 817–858.Google Scholar
Andrews, D. (1994) Empirical Process Methods in Econometrics. In R. Engle & D. McFadden (eds.), Handbook of Econometrics, Vol. 4, pp. 2248–2292. Amsterdam: North-Holland.
Andrews, D. (1999) Consistent moment selection procedures for generalized method of moments estimation. Econometrica 67, 543–564.Google Scholar
Andrews, D. & B. Lu (2001) Consistent model and moment selection procedures for GMM estimation with application to dynamic panel data models. Journal of Econometrics 101, 123–164.Google Scholar
Chernozhukov, V. & C. Hansen (2001) An IV Model of Quantile Treatment Effects. Working paper, Department of Economics, MIT.
Christoffersen, P.F., J. Hahn, & A. Inoue (2001) Testing and comparing value at risk measures. Journal of Empirical Finance 8, 325–342.Google Scholar
Hansen, L., J. Heaton, & A. Yaron (1996) Finite-sample properties of some alternative GMM estimators. Journal of Business and Economic Statistics 14, 262–280.Google Scholar
Imbens, G., R. Spady, & P. Johnson (1998) Information theoretic approaches to inference in moment condition models. Econometrica 66, 333–357.Google Scholar
Kitamura, Y. (1997) Empirical likelihood methods with weakly dependent processes. Annals of Statistics 25, 2084–2102.Google Scholar
Kitamura, Y. (2000) Comparing Misspecified Dynamic Econometric Models Using Nonparametric Likelihood. Working paper, Department of Economics, University of Wisconsin.
Kitamura, Y. & M. Stutzer (1997) An information-theoretic alternative to generalized method of moments estimation. Econometrica 65, 861–874.Google Scholar
Newey, W. & D. McFadden (1994) Large sample estimation and hypothesis testing. In R. Engle & D. McFadden (eds.), Handbook of Econometrics, vol. 4, pp. 2113–2241. Amsterdam: North-Holland.
Newey, W. & R. Smith (2000) Asymptotic Bias and Equivalence of GMM and GEL Estimators. Working paper 01/517, University of Bristol.
Newey, W. & K.D. West (1987) A simple, positive semi-definite heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica 55, 703–708.Google Scholar
Pötscher, B. (1991) Effects of model selection on inference. Econometric Theory 7, 163–185.Google Scholar
Qin, J. & J. Lawless (1994) Empirical likelihood and general estimating equations. Annals of Statistics 22, 300–325.Google Scholar
Ramalho, J.J. & R.J. Smith (2002) Generalized empirical likelihood non-nested tests. Journal of Econometrics 102, 1–28.Google Scholar
Smith, R.J. (1997) Alternative semiparametric likelihood approaches to generalised method of moments estimation. Economics Journal 107, 503–519.Google Scholar
Vuong, Q. (1989) Likelihood-ratio tests for model selection and non-nested hypotheses. Econometrica 57, 307–333.Google Scholar