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SIMPLE TWO-STAGE INFERENCE FOR A CLASS OF PARTIALLY IDENTIFIED MODELS

Published online by Cambridge University Press:  08 September 2014

Xiaoxia Shi  and
Matthew Shum
Xiaoxia Shi*
Affiliation:
University of Wisconsin at Madison
Matthew Shum*
Affiliation:
California Institute of Technology
*
*Address correspondence to Xiaoxia Shi, Department of Economics, University of Wisconsin, 1180 Observatory Drive, Madison, WI, 53706; e-mail: xshi@ssc.wisc.edu or to Matthew Shum, Division of Humanities and Social Sciences, California Institute of Technology, MC 228-77, 1200 East California Blvd., Pasadena, CA 91125; e-mail: mshum@caltech.edu.
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Abstract

This paper proposes a new two-stage estimation and inference procedure for a class of partially identified models. The procedure can be considered an extension of classical minimum distance estimation procedures to accommodate inequality constraints and partial identification. It involves no tuning parameter, is nonconservative, and is conceptually and computationally simple. The class of models includes models of interest to applied researchers, including the static entry game, a voting game with communication, and a discrete mixture model. Besides, a technical contribution is an implicit correspondence lemma which generalizes the implicit function theorem to multivalued implicit maps.

Type
ARTICLES
Information
Econometric Theory , Volume 31 , Issue 3 , June 2015 , pp. 493 - 520
Copyright
Copyright © Cambridge University Press 2014 

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Footnotes

We thank Yanqin Fan, Patrik Guggenberger, Bruce E. Hansen, Jack R. Porter, the editor Peter C.B. Phillips, the co-editor, and two anonymous referees for useful comments and suggestions. Xiaoxia Shi acknowledges the financial support of the Wisconsin Alumni Research Foundation via the Graduate School Fall Competition Award.

References

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