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A tractable estimator for general mixed multinomial logit models

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  • Jonathan James
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    Abstract

    The mixed logit is a framework for incorporating unobserved heterogeneity in discrete choice models in a general way. These models are difficult to estimate because they result in a complicated incomplete data likelihood. This paper proposes a new approach for estimating mixed logit models. The estimator is easily implemented as iteratively re-weighted least squares: the well known solution for complete data likelihood logits. The main benefit of this approach is that it requires drastically fewer evaluations of the simulated likelihood function, making it significantly faster than conventional methods that rely on numerically approximating the gradient. The method is rooted in a generalized expectation and maximization (GEM) algorithm, so it is asymptotically consistent, efficient, and globally convergent.

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    File URL: http://www.clevelandfed.org/research/workpaper/2012/wp1219.pdf
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    Bibliographic Info

    Paper provided by Federal Reserve Bank of Cleveland in its series Working Paper with number 1219.

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    Date of creation: 2012
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    Handle: RePEc:fip:fedcwp:1219

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    Related research

    Keywords: Econometrics ; Econometric models;

    This paper has been announced in the following NEP Reports:

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    1. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, December.
    2. Peter Arcidiacono & John Bailey Jones, 2003. "Finite Mixture Distributions, Sequential Likelihood and the EM Algorithm," Econometrica, Econometric Society, vol. 71(3), pages 933-946, 05.
    3. Nielsen, Soren Feodor, 2000. "On simulated EM algorithms," Journal of Econometrics, Elsevier, vol. 96(2), pages 267-292, June.
    4. Bernal, Raquel & Keane, Michael P., 2010. "Quasi-structural estimation of a model of childcare choices and child cognitive ability production," Journal of Econometrics, Elsevier, vol. 156(1), pages 164-189, May.
    5. Dankmar Böhning & Bruce Lindsay, 1988. "Monotonicity of quadratic-approximation algorithms," Annals of the Institute of Statistical Mathematics, Springer, vol. 40(4), pages 641-663, December.
    6. Dankmar Böhning, 1992. "Multinomial logistic regression algorithm," Annals of the Institute of Statistical Mathematics, Springer, vol. 44(1), pages 197-200, March.
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