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Estimation of a constrained multinomial logit model

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  • Marisol Castro
  • Francisco Martínez
  • Marcela Munizaga

Abstract

Identifying the set of available alternatives in a choice process after considering an individual’s bounds or thresholds is a complex process that, in practice, is commonly simplified by assuming exogenous rules in the choice set formation. The Constrained Multinomial Logit (CMNL) model incorporates thresholds in several attributes as a key endogenous process to define the alternatives choice/rejection mechanism. The model allows for the inclusion of multiple constraints and has a closed form. In this paper, we study the estimation of the CMNL model using the maximum likelihood function, develop a methodology to estimate the model overcoming identification problems by an endogenous partition of the sample, and test the model estimation with both synthetic and real data. The CMNL model appears to be suitable for general applications as it presents a significantly better fit than the MNL model under constrained behaviour and replicates the MNL estimates in the unconstrained case. Using mode choice real data, we found significant differences in the values of times and elasticities between compensatory MNL and semi-compensatory CMNL models, which increase as the thresholds on attributes become active. Copyright Springer Science+Business Media, LLC. 2013

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  • Marisol Castro & Francisco Martínez & Marcela Munizaga, 2013. "Estimation of a constrained multinomial logit model," Transportation, Springer, vol. 40(3), pages 563-581, May.
    • Handle: RePEc:kap:transp:v:40:y:2013:i:3:p:563-581
    DOI: 10.1007/s11116-012-9435-4
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    References listed on IDEAS

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    1. Swait, Joffre, 2009. "Choice models based on mixed discrete/continuous PDFs," Transportation Research Part B: Methodological, Elsevier, vol. 43(7), pages 766-783, August.
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    6. Kaplan, Sigal & Shiftan, Yoram & Bekhor, Shlomo, 2012. "Development and estimation of a semi-compensatory model with a flexible error structure," Transportation Research Part B: Methodological, Elsevier, vol. 46(2), pages 291-304.
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