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Semiparametric estimation of random coefficients in structural economic models

Nesheim, LP; Hoderlein, S; Simoni, A; (2017) Semiparametric estimation of random coefficients in structural economic models. Econometric Theory , 33 (6) pp. 1265-1305. 10.1017/S0266466616000396. Green open access

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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.

Type: Article
Title: Semiparametric estimation of random coefficients in structural economic models
Open access status: An open access version is available from UCL Discovery
DOI: 10.1017/S0266466616000396
Publisher version: https://doi.org/10.1017/S0266466616000396
Language: English
Additional information: © Cambridge University Press 2016
Keywords: Nonlinear random coefficients, mixture models, structural models, nonparametric, semiparametric, heterogeneity, inverse problems
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Economics
URI: https://discovery.ucl.ac.uk/id/eprint/1518071
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