Robust Semiparametric Estimation in Panel Multinomial Choice Models

53 Pages Posted: 12 Dec 2018 Last revised: 23 Aug 2021

See all articles by Wayne Yuan Gao

Wayne Yuan Gao

University of Pennsylvania - Department of Economics

Ming Li

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

Date Written: March 27, 2021

Abstract

This paper proposes a robust method for semiparametric identification and estimation in panel multinomial choice models, where we allow for infinite-dimensional fixed effects that enter into consumer utilities in an additively nonseparable way, thus incorporating rich forms of unobserved heterogeneity. Our identification strategy exploits multivariate monotonicity in parametric indexes, and uses the logical contraposition of an intertemporal inequality on choice probabilities to obtain identifying restrictions. We provide a consistent estimation procedure, and demonstrate the practical advantages of our method with simulations and an empirical illustration with the Nielsen data.

Keywords: semiparametric estimation, panel multinomial choice, nonparametric unobserved heterogeneity, nonseparability, multivariate monotonicity

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

Suggested Citation

Gao, Wayne and Li, Ming, Robust Semiparametric Estimation in Panel Multinomial Choice Models (March 27, 2021). Available at SSRN: https://ssrn.com/abstract=3282293 or http://dx.doi.org/10.2139/ssrn.3282293

Wayne Gao (Contact Author)

University of Pennsylvania - Department of Economics ( email )

Ronald O. Perelman Center for Political Science
133 South 36th Street
Philadelphia, PA 19104-6297
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

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
77
Abstract Views
559
rank
419,746
PlumX Metrics