Robust Semiparametric Estimation in Panel Multinomial Choice Models
53 Pages Posted: 12 Dec 2018 Last revised: 23 Aug 2021
Date Written: March 27, 2021
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
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