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

Benjamin J Gillen, Sergio Montero, Hyungsik Moon () and Matthew Shum

The Econometrics Journal, 2019, vol. 22, issue 3, 262-281

Abstract: SummaryWe 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 for market-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 from Mexican elections.

Keywords: Random-coefficients logit model; high-dimensional regressors; LASSO; elections; machine learning; big data (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://hdl.handle.net/10.1093/ectj/utz010 (application/pdf)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:emjrnl:v:22:y:2019:i:3:p:262-281.

Access Statistics for this article

The Econometrics Journal is currently edited by Jaap Abbring

More articles in The Econometrics Journal from Royal Economic Society Contact information at EDIRC.
Bibliographic data for series maintained by Oxford University Press ().