Demand Estimation with High-Dimensional Product Characteristics

Benjamin J. Gillen, Matthew Shum () and Hyungsik Moon ()

A chapter in Bayesian Model Comparison, 2014, vol. 34, pp 301-323 from Emerald Group Publishing Limited

Abstract: Structural models of demand founded on the classic work of Berry, Levinsohn, and Pakes (1995) link variation in aggregate market shares for a product to the influence of product attributes on heterogeneous consumer tastes. We consider implementing these models in settings with complicated products where consumer preferences for product attributes are sparse, that is, where a small proportion of a high-dimensional product characteristics influence consumer tastes. We propose a multistep estimator to efficiently perform uniform inference. Our estimator employs a penalized pre-estimation model specification stage to consistently estimate nonlinear features of the BLP model. We then perform selection via a Triple-LASSO for explanatory controls, treatment selection controls, and instrument selection. After selecting variables, we use an unpenalized GMM estimator for inference. Monte Carlo simulations verify the performance of these estimators.

Keywords: BLP demand model; high-dimensional product characteristics; LASSO; Post-LASSO; shrinkage estimation; L15; C01; C26; C55 (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-905320140000034020

DOI: 10.1108/S0731-905320140000034020

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