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Semiparametric Identification and Estimation of Multinomial Discrete Choice Models using Error Symmetry

Author

Listed:
  • Arthur Lewbel

    (Boston College)

  • Jin Yan

    (Chinese University of Hong Kong)

  • Yu Zhou

    (School of Economics, Fudan University)

Abstract

We provide a new method to point identify and estimate cross-sectional multinomial choice models, using conditional error symmetry. Our model nests common random coefficient specifications (without having to specify which regressors have random coefficients), and more generally allows for arbitrary heteroskedasticity on most regressors, unknown error distribution, and does not require a "large support" "(such as identification at infinity) assumption. We propose an estimator that minimizes the squared di§erences of the estimated error density at pairs of symmetric points about the origin. Our estimator is root N consistent and asymptotically normal, making statistical inference straightforward.

Suggested Citation

  • Arthur Lewbel & Jin Yan & Yu Zhou, 2021. "Semiparametric Identification and Estimation of Multinomial Discrete Choice Models using Error Symmetry," Boston College Working Papers in Economics 1028, Boston College Department of Economics, revised 15 Dec 2021.
    • Handle: RePEc:boc:bocoec:1028
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    References listed on IDEAS

    as
    1. Han Hong & Elie Tamer, 2003. "Inference in Censored Models with Endogenous Regressors," Econometrica, Econometric Society, vol. 71(3), pages 905-932, May.
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    Cited by:

    1. Nail Kashaev, 2018. "Identification and estimation of multinomial choice models with latent special covariates," Papers 1811.05555, arXiv.org, revised Mar 2022.

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    More about this item

    Keywords

    Central Symmetry; Exclusion Restriction; Multinomial Discrete Choice;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

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