Essays in Econometrics and Industrial Organization

Abstract

This thesis includes three essays in Econometrics related to Demand Estimation, a central tool in Industrial Organization. The first essay develops a flexible discrete- choice demand framework for aggregate data sets that extends Berry, Levinsohn and Pakes (1995) and the Pure Characteristics Demand Model of Berry and Pakes (2007). The framework accommodates zero market shares, which are a challenge for alternative approaches. I show that zeros in demand generate an endogenously censored model, which leads to moment inequalities. I provide a simple, compu- tationally tractable, asymptotically normal estimator based on two contributions: a globally-convergent algorithm to recover utilities from observed demand and a Quasi-Bayes approach that minimizes simulation variance. The second essay applies the method from the first to a internal migration dataset in the US. In particular, I study moving costs and housing policies on US internal migration data. Moving costs are high and highly variable, which implies substantial benefits from targeted housing policies. The third essay extends the standard demand model allowing for nonparametric random coefficients and heteroskedastic error distributions.