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Nonparametric identification and estimation of random coefficients in multinomial choice models

Nonparametric identification and estimation of random coefficients in multinomial choice models We show how to nonparametrically identify the distribution of unobservables, such as random coefficients, that characterizes the heterogeneity among consumers in multinomial choice models. We provide general identification conditions for a class of nonlinear models and then verify these conditions using the primitives of the multinomial choice model. We require that the distribution of unobservables lie in the class of all distributions with finite support, which under our most general assumptions, resembles a product space where some of the product members are function spaces. We show how identification leads to the consistency of a nonparametric estimator. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Rand Journal of Economics Wiley

Nonparametric identification and estimation of random coefficients in multinomial choice models

The Rand Journal of Economics , Volume 47 (1) – Feb 1, 2016

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References (73)

Publisher
Wiley
Copyright
© 2016 The RAND Corporation
ISSN
0741-6261
eISSN
1756-2171
DOI
10.1111/1756-2171.12125
Publisher site
See Article on Publisher Site

Abstract

We show how to nonparametrically identify the distribution of unobservables, such as random coefficients, that characterizes the heterogeneity among consumers in multinomial choice models. We provide general identification conditions for a class of nonlinear models and then verify these conditions using the primitives of the multinomial choice model. We require that the distribution of unobservables lie in the class of all distributions with finite support, which under our most general assumptions, resembles a product space where some of the product members are function spaces. We show how identification leads to the consistency of a nonparametric estimator.

Journal

The Rand Journal of EconomicsWiley

Published: Feb 1, 2016

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