identification of discrete choice models of demand, we exploit shape restrictions on demand implied by discrete choice to generate a …In this paper we introduce a new approach to estimating a differentiated product demand system that allows for error in … demand estimation techniques. Although we find that error in market shares generally undermine the standard point …

In this paper, we introduce a new approach to estimating differentiated product demand systems that allows for products … with zero sales in the data. Zeroes in demand are a common problem in differentiated product markets, but fall outside the … scope of existing demand estimation techniques. We show that with a lower bound imposed on the expected sales quantities, we …

for estimating demand elasticities. Soderbery (2015) extended this estimator and created a routine which was shown to be …

This paper introduces a panel GMM framework for identifying and estimating demand elasticities via heteroscedasticity …

We propose a demand model where consumers simultaneously choose a few different goodsfrom a large menu of available … goods, and choose how much to consume of each good. Themodel nests multinomial discrete choice and continuous demand systems … purchasing different numbers of typesof goods. We show semiparametric identification of the model. We apply the model to …

waves. The empirical focus concerns the estimation of a structural demand model for the US railroad industry. Then, the … demand estimates are used to compute the evolution of the mark-ups, the quality of the freight services provided, and the … explicitly into account this attrition issue. I find that the algorithm produces more plausible estimates of demand coefficients …

has documented that finite-sample bias in the demand estimation problem of Berry, Levinsohn, and Pakes (1995) is often …