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A Multivariate Model for Multinomial Choices

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  • Bel, K.
  • Paap, R.

Abstract

__Abstract__ Multinomial choices of individuals are likely to be correlated. Nonetheless, econometric models for this phenomenon are scarce. A problem of multivariate multinomial choice models is that the number of potential outcomes can become very large which makes parameter interpretation and inference difficult. We propose a novel Multivariate Multinomial Logit specification, where (i) the number of parameters stays limited; (ii) there is a clear interpretation of the parameters in terms of odds ratios; (iii) zero restrictions on parameters result in independence between the multinomial choices and; (iv) parameter inference is feasible using a composite likelihood approach even if the multivariate dimension is large. Finally, these nice properties are also valid in a fixed-effects panel version of the model.

Suggested Citation

  • Bel, K. & Paap, R., 2014. "A Multivariate Model for Multinomial Choices," Econometric Institute Research Papers EI 2014-26, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:77168
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    References listed on IDEAS

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    7. Geweke, John & Keane, Michael P & Runkle, David, 1994. "Alternative Computational Approaches to Inference in the Multinomial Probit Model," The Review of Economics and Statistics, MIT Press, vol. 76(4), pages 609-632, November.
    8. Burda, Martin & Harding, Matthew & Hausman, Jerry, 2008. "A Bayesian mixed logit-probit model for multinomial choice," Journal of Econometrics, Elsevier, vol. 147(2), pages 232-246, December.
    9. Koen Bel & Dennis Fok & Richard Paap, 2018. "Parameter estimation in multivariate logit models with many binary choices," Econometric Reviews, Taylor & Francis Journals, vol. 37(5), pages 534-550, May.
    10. Gary Chamberlain, 1980. "Analysis of Covariance with Qualitative Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 47(1), pages 225-238.
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    Keywords

    Discrete Choices; Multivariate analysis; Multinomial Logit; Composite Likelihood;
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