BLP-2LASSO for aggregate discrete choice models with rich covariates

Benjamin J. Gillen, Sergio Montero, Hyungsik Roger Moon, Matthew Shum

Research output: Contribution to journalArticle

Abstract

We introduce the BLP-2LASSO model, which augments the classic BLP (Berry, Levinsohn, and Pakes, 1995) random-coefficients logit model to allow for data-driven selection among a high-dimensional set of control variables using the 'double-LASSO' procedure proposed by Belloni, Chernozhukov, and Hansen (2013). Economists often study consumers' aggregate behaviour across markets choosing from a menu of differentiated products. In this analysis, local demographic characteristics can serve as controls formarket-specific preference heterogeneity. 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 from the recent literature in high-dimensional econometrics. Our application explores the effect of campaign spending on vote shares in data fromMexican elections.

Original languageEnglish
Pages (from-to)262-281
Number of pages20
JournalEconometrics Journal
Volume22
Issue number3
DOIs
Publication statusPublished - 2019 Sep 1

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All Science Journal Classification (ASJC) codes

  • Economics and Econometrics

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