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Semi-nonparametric estimation of random coefficients logit model for aggregate demand

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  • Lu, Zhentong
  • Shi, Xiaoxia
  • Tao, Jing

Abstract

In this paper, we propose a two-step semi-nonparametric estimator for the widely used random coefficients logit demand model. The approach applies to the same setup as Berry et al. (1995, BLP)-type of models with many products, but has the advantage of not requiring computing demand inversion. In particular, the first step of our approach estimates the fixed coefficients via a computationally very easy linear sieve generalized method of moments (GMM). The second step uncovers the distribution of the random coefficient via a sieve minimum distance or GMM procedure. We show identification and derive the asymptotic properties of the estimator in a large market environment. Monte Carlo simulations and empirical illustrations support the theoretical results and demonstrate the usefulness of our estimator in practice.

Suggested Citation

  • Lu, Zhentong & Shi, Xiaoxia & Tao, Jing, 2023. "Semi-nonparametric estimation of random coefficients logit model for aggregate demand," Journal of Econometrics, Elsevier, vol. 235(2), pages 2245-2265.
    • Handle: RePEc:eee:econom:v:235:y:2023:i:2:p:2245-2265
    DOI: 10.1016/j.jeconom.2022.10.011
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    More about this item

    Keywords

    Demand estimation; Differentiated products; Random coefficients logit; Semi-nonparametric estimation;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • L10 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - General
    • L62 - Industrial Organization - - Industry Studies: Manufacturing - - - Automobiles; Other Transportation Equipment; Related Parts and Equipment

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