BLP-Lasso for Aggregate Discrete Choice Models of Elections with Rich Demographic Covariates

Abstract

Economists often study consumers’ aggregate behavior across markets choosing from a menu of differentiated products. In this analysis, local demographic characteristics can serve as controls for market-specific heterogeneity in product preferences. Given rich demographic data, implementing these models requires specifying which variables to include in the analysis, an ad hoc process typically guided primarily by a researcher’s intuition. We propose a data-driven approach to estimate these models applying penalized estimation algorithms imported from the machine learning literature along with confidence intervals that are robust to variable selection. Our application explores the effect of campaign spending on vote shares in data from Mexican elections.