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Methods of determining choice probability in utility maximising multiple alternative models

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  • Langdon, M. G.

Abstract

Methods of estimating choice probabilities between multiple alternatives are described, within the context of various methods of specifying the degree of correlation between the alternatives. A utility maximising framework is assumed. The models described are based either on the logit family or multinomial probit. Two new methods of analytical approximation for the multinomial probit model are introduced, which appear to show significant advantages over the traditional Clark method. Some comparative results of choice probability estimates are presented to support this contention. The conclusion discusses the relative usefulness of different choice estimation methods for different types of problem.

Suggested Citation

  • Langdon, M. G., 1984. "Methods of determining choice probability in utility maximising multiple alternative models," Transportation Research Part B: Methodological, Elsevier, vol. 18(3), pages 209-234, June.
    • Handle: RePEc:eee:transb:v:18:y:1984:i:3:p:209-234
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    Cited by:

    1. Borsch-Supan, Axel & Hajivassiliou, Vassilis A., 1993. "Smooth unbiased multivariate probability simulators for maximum likelihood estimation of limited dependent variable models," Journal of Econometrics, Elsevier, vol. 58(3), pages 347-368, August.
    2. Axel Borsch-Supan, 1998. "Incentive Effects of Social Security on Labor Force Participation: Evidence in Germany and Across Europe," NBER Working Papers 6780, National Bureau of Economic Research, Inc.
    3. Axel Borsch-Supan & Vassilis Hajivassiliou & Laurence J. Kotlikoff, 1992. "Health, Children, and Elderly Living Arrangements: A Multiperiod-Multinomial Probit Model with Unobserved Heterogeneity and Autocorrelated Errors," NBER Chapters, in: Topics in the Economics of Aging, pages 79-108, National Bureau of Economic Research, Inc.
    4. Martinetti, Davide & Geniaux, Ghislain, 2017. "Approximate likelihood estimation of spatial probit models," Regional Science and Urban Economics, Elsevier, vol. 64(C), pages 30-45.

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