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Fitting nonparametric mixed logit models via expectation-maximization algorithm

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  • Daniele Pacifico

    (Italian Department of the Treasury)

Abstract

In this article, I provide an illustrative, step-by-step implementation of the expectation-maximization algorithm for the nonparametric estimation of mixed logit models. In particular, the proposed routine allows users to fit straightforwardly latent-class logit models with an increasing number of mass points so as to approximate the unobserved structure of the mixing distribution. Copyright 2012 by StataCorp LP.

Suggested Citation

  • Daniele Pacifico, 2012. "Fitting nonparametric mixed logit models via expectation-maximization algorithm," Stata Journal, StataCorp LP, vol. 12(2), pages 284-298, June.
    • Handle: RePEc:tsj:stataj:v:12:y:2012:i:2:p:284-298
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    References listed on IDEAS

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    1. Joel Huber and Kenneth Train., 2000. "On the Similarity of Classical and Bayesian Estimates of Individual Mean Partworths," Economics Working Papers E00-289, University of California at Berkeley.
    2. Greene, William H. & Hensher, David A., 2003. "A latent class model for discrete choice analysis: contrasts with mixed logit," Transportation Research Part B: Methodological, Elsevier, vol. 37(8), pages 681-698, September.
    3. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, September.
    4. Arne Risa Hole, 2007. "Fitting mixed logit models by using maximum simulated likelihood," Stata Journal, StataCorp LP, vol. 7(3), pages 388-401, September.
    5. 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.
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    Cited by:

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