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SEMIPARAMETRIC ESTIMATION OF RANDOM COEFFICIENTS IN STRUCTURAL ECONOMIC MODELS

Hoderlein, S; Nesheim, L; Simoni, A; (2016) SEMIPARAMETRIC ESTIMATION OF RANDOM COEFFICIENTS IN STRUCTURAL ECONOMIC MODELS. Econometric Theory pp. 1-41. 10.1017/S0266466616000396. (In press).

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Abstract

Copyright © Cambridge University Press 2016This 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.

Type: Article
Title: SEMIPARAMETRIC ESTIMATION OF RANDOM COEFFICIENTS IN STRUCTURAL ECONOMIC MODELS
DOI: 10.1017/S0266466616000396
UCL classification: UCL > School of Arts and Social Sciences
URI: http://discovery.ucl.ac.uk/id/eprint/1362233
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