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Mixed MNL models for discrete response

Author

Listed:
  • Daniel McFadden

    (Department of Economics, University of California, Berkeley, CA, 94720-3880, USA)

  • Kenneth Train

    (Department of Economics, University of California, Berkeley, CA, 94720-3880, USA)

Abstract

This paper considers mixed, or random coefficients, multinomial logit (MMNL) models for discrete response, and establishes the following results. Under mild regularity conditions, any discrete choice model derived from random utility maximization has choice probabilities that can be approximated as closely as one pleases by a MMNL model. Practical estimation of a parametric mixing family can be carried out by Maximum Simulated Likelihood Estimation or Method of Simulated Moments, and easily computed instruments are provided that make the latter procedure fairly efficient. The adequacy of a mixing specification can be tested simply as an omitted variable test with appropriately defined artificial variables. An application to a problem of demand for alternative vehicles shows that MMNL provides a flexible and computationally practical approach to discrete response analysis. Copyright © 2000 John Wiley & Sons, Ltd.

Suggested Citation

  • Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
    • Handle: RePEc:jae:japmet:v:15:y:2000:i:5:p:447-470
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    References listed on IDEAS

    as
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