A simple nonparametric approach to estimating the distribution of random coefficients in structural models

Jeremy Fox, Kyoo il Kim () and Chenyu Yang

Journal of Econometrics, 2016, vol. 195, issue 2, 236-254

Abstract: We explore least squares and likelihood nonparametric mixtures estimators of the joint distribution of random coefficients in structural models. The estimators fix a grid of heterogeneous parameters and estimate only the weights on the grid points, an approach that is computationally attractive compared to alternative nonparametric estimators. We provide conditions under which the estimated distribution function converges to the true distribution in the weak topology on the space of distributions. We verify most of the consistency conditions for three discrete choice models. We also derive the convergence rates of the least squares nonparametric mixtures estimator under additional restrictions. We perform a Monte Carlo study on a dynamic programming model.

Keywords: Random coefficients; Mixtures; Discrete choices; Dynamic programming; Sieve estimation (search for similar items in EconPapers)
JEL-codes: C21 C22 C25 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (26)

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Related works:
Working Paper: A Simple Nonparametric Approach to Estimating the Distribution of Random Coefficients in Structural Models (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:195:y:2016:i:2:p:236-254

DOI: 10.1016/j.jeconom.2016.05.018

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