Robust Semiparametric Estimation in Panel Multinomial Choice Models

76 Pages Posted: 12 Dec 2018 Last revised: 7 Mar 2019

See all articles by Wayne Yuan Gao

Wayne Yuan Gao

Yale University, Faculty of Arts & Sciences, Department of Economics

Ming Li

Yale University, Faculty of Arts & Sciences, Department of Economics

Date Written: January 31, 2019

Abstract

This paper proposes a simple and robust method for semiparametric identification and estimation in a panel multinomial choice model, where we allow for infinite-dimensional fixed effects that enter into consumer utilities in an additively nonseparabe way, thus incorporating rich forms of unobserved heterogeneity. Our identification strategy exploits multivariate monotonicity in an index vector of observable characteristics, and uses the logical contraposition of an intertemporal inequality on choice probabilities to obtain identifying restrictions on the indexes. We provide consistent estimators based on our identification strategy, together with a computational procedure that exploits a combination of theoretical and practical advantages under a spherical-coordinate reparameterization. A simulation study and an empirical illustration with the Nielsen data are conducted to analyze the finite-sample performance of our estimation method and demonstrate the adequacy of our computational procedure for practical implementation.

Keywords: semiparametric estimation, panel multinomial choice, infinite-dimensional unobserved heterogeneity, nonseparability, monotonicity, spherical coordinates

JEL Classification: C01, C14, C23, C63, L81, M31

Suggested Citation

Gao, Wayne and Li, Ming, Robust Semiparametric Estimation in Panel Multinomial Choice Models (January 31, 2019). Available at SSRN: https://ssrn.com/abstract=3282293 or http://dx.doi.org/10.2139/ssrn.3282293

Wayne Gao (Contact Author)

Yale University, Faculty of Arts & Sciences, Department of Economics ( email )

28 Hillhouse Ave
New Haven, CT 06511
United States

HOME PAGE: http://www.waynegao.com

Ming Li

Yale University, Faculty of Arts & Sciences, Department of Economics ( email )

28 Hillhouse Ave
New Haven, CT 06520-8268
United States

HOME PAGE: http://ming-li.net

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