Semi-Nonparametric Estimation of Random Coefficient Logit Model for Aggregate Demand
98 Pages Posted: 17 Dec 2019 Last revised: 7 Oct 2022
Date Written: February 6, 2021
Abstract
In this paper, we propose a two-step semi-nonparametric estimator for the widely used random coefficients logit demand model. The approach applies to the same setup as Berry, Levinsohn, and Pakes (1995, BLP)-type of models with many products, but has the advantage of not requiring computing demand inversion. In particular, the first step of our approach estimates the fixed coefficients via a computationally very easy linear sieve generalized method of moments (GMM).
The second step uncovers the distribution of the random coefficient via a sieve minimum distance or GMM procedure. We show identification and derive the asymptotic properties of the estimator in a large market environment. Monte Carlo simulations and empirical illustrations support the theoretical results and demonstrate the usefulness of our estimator in practice.
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