Estimating High-Dimensional Discrete Choice Model of Differentiated Products with Random Coefficients
Econometrics
2020-04-21 v1
Abstract
We propose an estimation procedure for discrete choice models of differentiated products with possibly high-dimensional product attributes. In our model, high-dimensional attributes can be determinants of both mean and variance of the indirect utility of a product. The key restriction in our model is that the high-dimensional attributes affect the variance of indirect utilities only through finitely many indices. In a framework of the random-coefficients logit model, we show a bound on the error rate of a -regularized minimum distance estimator and prove the asymptotic linearity of the de-biased estimator.
Keywords
Cite
@article{arxiv.2004.08791,
title = {Estimating High-Dimensional Discrete Choice Model of Differentiated Products with Random Coefficients},
author = {Masayuki Sawada and Kohei Kawaguchi},
journal= {arXiv preprint arXiv:2004.08791},
year = {2020}
}