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SEMIPARAMETRIC ESTIMATION OF RANDOM COEFFICIENTS IN STRUCTURAL ECONOMIC MODELS

Published online by Cambridge University Press:  02 November 2016

Stefan Hoderlein,
Lars Nesheim and
Anna Simoni
Stefan Hoderlein
Affiliation:
Boston College
Lars Nesheim*
Affiliation:
University College London
Anna Simoni
Affiliation:
CNRS-CREST
*
*Address correspondence to Lars Nesheim, Department of Economics, University College London, Gower Street, London WC1E 6BT, UK; e-mail: l.nesheim@ucl.ac.uk.
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Abstract

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This paper discusses nonparametric estimation of the distribution of random coefficients in a structural model that is nonlinear in the random coefficients. We establish that the problem of recovering the probability density function (pdf) of random parameters falls into the class of convexly-constrained inverse problems. The framework offers an estimation method that separates computational solution of the structural model from estimation. We first discuss nonparametric identification. Then, we propose two alternative estimation procedures to estimate the density and derive their asymptotic properties. Our general framework allows us to deal with unobservable nuisance variables, e.g., measurement error, but also covers the case when there are no such nuisance variables. Finally, Monte Carlo experiments for several structural models are provided which illustrate the performance of our estimation procedure.

Information

Type
ARTICLES
Information
Econometric Theory , Volume 33 , Issue 6 , December 2017, pp. 1265 - 1305
Copyright
Copyright © Cambridge University Press 2016 

Footnotes

We thank the co-editor Yoon-Jae Whang and three anonymous referees whose comments greatly improved the paper. We have benefited from comments and discussions with Victor Aguirregabiria, Orazio Attanasio, Richard Blundell, Chris Carroll, Jeremy Fox, Emmanuel Guerre, Dirk Krueger, Arthur Lewbel, Enno Mammen, Rosa Matzkin, Peter Phillips, Jean-Marc Robin, Sami Stouli, Yuanyuan Wan, and Hal White. Lars Nesheim gratefully acknowledges financial support from the UK Economic and Social Research Council through the ESRC Centre for Microdata Methods and Practice grant RES-589-28-0001. Anna Simoni gratefully acknowledges financial support from the University of Mannheim through SFB 884 and from ANR-13-BSH1-0004, and ANR-11-LABEX-0047.

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