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Demand Estimation with High-Dimensional Product Characteristics

In: Bayesian Model Comparison

Author

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  • Benjamin J. Gillen
  • Matthew Shum
  • Hyungsik Roger Moon

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.

Suggested Citation

  • Benjamin J. Gillen & Matthew Shum & Hyungsik Roger Moon, 2014. "Demand Estimation with High-Dimensional Product Characteristics," Advances in Econometrics, in: Bayesian Model Comparison, volume 34, pages 301-323, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:aecozz:s0731-905320140000034020
    DOI: 10.1108/S0731-905320140000034020
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    Citations

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    Cited by:

    1. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015. "Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments," American Economic Review, American Economic Association, vol. 105(5), pages 486-490, May.
    2. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015. "Valid Post-Selection and Post-Regularization Inference: An Elementary, General Approach," Annual Review of Economics, Annual Reviews, vol. 7(1), pages 649-688, August.

    More about this item

    Keywords

    BLP demand model; high-dimensional product characteristics; LASSO; Post-LASSO; shrinkage estimation; L15; C01; C26; C55;
    All these keywords.

    JEL classification:

    • L15 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Information and Product Quality
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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