Gillen, Benjamin J. and Montero, Sergio and Moon, Hyungsik Roger and Shum, Matthew (2019) BLP-2LASSO for aggregate discrete choice models with rich covariates. Econometric Theory, 22 (3). pp. 262-281. ISSN 0266-4666. doi:10.1093/ectj/utz010. https://resolver.caltech.edu/CaltechAUTHORS:20200213-100221782
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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 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.
| Item Type: | Article | ||||||
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| Additional Information: | © 2019 Royal Economic Society. Published by Oxford University Press on behalf of Royal Economic Society. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model). Received: 11 October 2018; Accepted: 14 January 2019; Published: 11 July 2019. Co-editor Victor Chernozhukov handled this manuscript. We owe special thanks to Alexander Charles Smith for important insights early in developing the project. We are grateful for comments from David Brownstone, Martin Burda, Garland Durham, Jeremy Fox, Gautam Gowrisankaran, Chris Hansen, Stefan Holderlein, Ivan Jeliazkov, Dale Poirier, Guillame Weisang, Frank Windmeijer, and seminar participants at the Advances in Econometrics Conference on Bayesian Model Comparison, the California Econometrics Conference, Emory Political Science Conference, Stanford SITE, ASSA and APSA meetings, UC Irvine, Cal Poly San Luis Obispo, and the University of Arizona. | ||||||
| Subject Keywords: | Random-coefficients logit model, high-dimensional regressors, LASSO, elections, machine learning, big data | ||||||
| Issue or Number: | 3 | ||||||
| Classification Code: | JEL: C55 - Large Data Sets: Modeling and Analysis | ||||||
| DOI: | 10.1093/ectj/utz010 | ||||||
| Record Number: | CaltechAUTHORS:20200213-100221782 | ||||||
| Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20200213-100221782 | ||||||
| Official Citation: | Benjamin J Gillen, Sergio Montero, Hyungsik Roger Moon, Matthew Shum, BLP-2LASSO for aggregate discrete choice models with rich covariates, The Econometrics Journal, Volume 22, Issue 3, September 2019, Pages 262–281, https://doi.org/10.1093/ectj/utz010 | ||||||
| Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||
| ID Code: | 101269 | ||||||
| Collection: | CaltechAUTHORS | ||||||
| Deposited By: | Tony Diaz | ||||||
| Deposited On: | 13 Feb 2020 19:31 | ||||||
| Last Modified: | 16 Nov 2021 18:01 |
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