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Unobserved Heterogeneity in Dynamic Discrete Choice Models

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  • S Reader

    (Department of Geography, McMaster University, Hamilton, Ontario L8S 4K1, Canada)

Abstract

Monte Carlo simulation methods are used to confirm the identifiability of discrete choice models in which unobserved heterogeneity is specified as a random effect and modelled using the nonparametric mass-points approach. This simulation analysis is also used to examine alternative strategies for the estimation of such models by using a quasi-Newton maximum-likelihood estimation procedure, given the apparent sensitivity of model identification to choice of starting values. A mass-point model approach is then applied to a dataset of repeated choice involving household shopping trips between three types of retail centre, and the results from this approach are compared with those obtained from a conventional cross-sectional multinomial logit choice model as well as to results from a model in which a parametric distribution (the Dirichlet ) is used to model the unobserved heterogeneity.

Suggested Citation

  • S Reader, 1993. "Unobserved Heterogeneity in Dynamic Discrete Choice Models," Environment and Planning A, , vol. 25(4), pages 495-519, April.
    • Handle: RePEc:sae:envira:v:25:y:1993:i:4:p:495-519
    DOI: 10.1068/a250495
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    References listed on IDEAS

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    Cited by:

    1. Michele Lalla & Francesco Pattarin, 2001. "Unemployment Duration: An Analysis of Incomplete, Completed, and Multiple Spells in Emilia-Romagna," Quality & Quantity: International Journal of Methodology, Springer, vol. 35(2), pages 203-230, May.
    2. González-Benito, Óscar & Santos-Requejo, Libia, 2002. "A comparison of approaches to exploit budget allocation data in cross-sectional maximum likelihood estimation of multi-attribute choice models," Omega, Elsevier, vol. 30(5), pages 315-324, October.
    3. Martínez, L. Miguel & Viegas, José Manuel, 2013. "A new approach to modelling distance-decay functions for accessibility assessment in transport studies," Journal of Transport Geography, Elsevier, vol. 26(C), pages 87-96.
    4. González-Benito, Óscar, 2004. "Random effects choice models: seeking latent predisposition segments in the context of retail store format selection," Omega, Elsevier, vol. 32(2), pages 167-177, April.
    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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