Highlights We derive a generalized Bayes estimator of a probit model with latent attributes. The estimator converges quick, taking 5–15min for 2500 observations. We account for simultaneity in the determination of the latent attributes. We analyze commuting behavior, vehicle choice, and the energy paradox. We derive implicit discount rates that allow for latent heterogeneity.
Abstract Discrete choice modeling is experiencing a reemergence of research interest in the inclusion of latent variables as explanatory variables of consumer behavior. There are several reasons that motivate the integration of latent attributes, including better-informed modeling of random consumer heterogeneity and treatment of endogeneity. However, current work still is at an early stage and multiple simplifying assumptions are usually imposed. For instance, most previous applications assume all of the following: independence of taste shocks and of latent attributes, exclusion restrictions, linearity of the effect of the latent attributes on the utility function, continuous manifest variables, and an a priori bound for the number of latent constructs. We derive and apply a structural choice model with a multinomial probit kernel and discrete effect indicators to analyze continuous latent segments of travel behavior, including inference on the energy paradox. Our estimator allows for interaction and simultaneity among the latent attributes, residual correlation, nonlinear effects on the utility function, flexible substitution patterns, and temporal correlation within responses of the same individual. Statistical properties of the Bayes estimator that we propose are exact and are not affected by the number of latent attributes.
Inference on mode preferences, vehicle purchases, and the energy paradox using a Bayesian structural choice model
Transportation Research Part B: Methodological ; 76 ; 1-26
2015-02-26
26 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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