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.