Generalized Empirical Likelihood-Based Model Selection Criteria for Moment Condition Models
- Creators
- Hong, Han
- Preston, Bruce
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Shum, Matthew
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.
Additional Information
© 2003 Cambridge University Press. Published online: 24 September 2003. 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.Attached Files
Published - HONet03.pdf
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Additional details
- Eprint ID
- 27255
- DOI
- 10.1017/S0266466603196028
- Resolver ID
- CaltechAUTHORS:20111017-113050114
- NSF
- SES-0079495
- NSF
- SES-0003352
- Fellowship of Woodrow Wilson Scholars
- Created
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2011-10-17Created from EPrint's datestamp field
- Updated
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2021-11-09Created from EPrint's last_modified field