Estimation of a constrained multinomial logit model
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.
General note
Artículo de publicación ISI
Patrocinador
Fondecyt (1110124, 1120288), ISCI (ICM P-05-004-F, CONICYT FBO16),
FONDEF D10I-1002. A previous version of this paper was presented in the International Choice Modelling
Conference.
Quote Item
Transportation (2013) 40:563–581
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