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Semiparametric Estimation of Random Coefficients in Structural Economic Models

Stefan Hoderlein, Lars Nesheim and Anna Simoni

No 895, Boston College Working Papers in Economics from Boston College Department of Economics

Abstract: 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.

Keywords: Nonlinear random coefficients; mixture models; structural models; heterogeneity; inverse problems (search for similar items in EconPapers)
Date: 2015-04-14, Revised 2016-02-01
New Economics Papers: this item is included in nep-dcm
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Related works:
Journal Article: SEMIPARAMETRIC ESTIMATION OF RANDOM COEFFICIENTS IN STRUCTURAL ECONOMIC MODELS (2017) Downloads
Working Paper: Semiparametric estimation of random coefficients in structural economic models (2012) Downloads
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