[数量经济学研讨会]A Semi-Nonparametric Estimator for Random-Coefficients Logit Demand Models



时间:5月16日 中午12:00 - 13:00

Title: A Semi-Nonparametric Estimator for Random-Coefficients Logit Demand Models (joint with X. Shi and J. Tao)

Abstract: In this paper, we study the semi-nonparametric identification and estimation of the widely used random coefficient logit demand models in a large market environment. Exploiting the structure of logit choice probabilities, we provide a simple argument to show the nonparametric identification of random coefficients and then propose a two-step semi-nonparametric estimator: In the first step, we transform the full demand system into a partial linear model and estimate the fixed (non-random) coefficients using standard linear sieve GMM; we further show that the estimator of the fixed coefficients is J -consistent under mild conditions. In the second step, we construct a sieve minimum distance/GMM estimator to uncover the distribution of random coefficients nonparametrically and demonstrate its consistency. We perform a set of Monte Carlo simulations to examine the finite sample properties of the estimator and apply it to the original BLP auto data and China auto market to illustrate the usefulness of our estimator. 


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