Estimation of a constrained multinomial logit model

Castro M.; Martinez F.; Munizaga, MA


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.

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Título según WOS: Estimation of a constrained multinomial logit model
Título según SCOPUS: Estimation of a constrained multinomial logit model
Título de la Revista: TRANSPORTATION
Volumen: 40
Número: 3
Fecha de publicación: 2013
Página de inicio: 563
Página final: 581
Idioma: English