Denis Bolduc

Hybrid Choice Models: Progress and Challenges

We discuss the development of predictive choice models that go beyond the random utility model in its narrowest formulation. Such approaches incorporate several elements of cognitive process that have been identified as important to the choice process, including strong dependence on history and context, perception formation, and latent constraints. A flexible and practical hybrid choice model is presented that integrates many types of discrete choice modeling methods, draws on different types of data, and allows for flexible disturbances and explicit modeling of latent psychological explanatory variables, heterogeneity, and latent segmentation. Both progress and challenges related to the development of the hybrid choice model are presented.

Extended Framework for Modeling Choice Behavior

We review the case against the standard model of rational behavior and discuss the consequences of various ‘anomalies’ of preference elicitation. A general theoretical framework that attempts to disentangle the various psychological elements in the decision-making process is presented. We then present a rigorous and general methodology to model the theoretical framework, explicitly incorporating psychological factors and their influences on choices. This theme has long been deemed necessary by behavioral researchers, but is often ignored in demand models. The methodology requires the estimation of an integrated multi-equation model consisting of a discrete choice model and the latent variable model system. We conclude with a research agenda to bring the theoretical framework into fruition.

Incorporating pro-environmental preferences towards green automobile technologies through a Bayesian hybrid choice model

In this article we develop, implement and apply a Markov chain Monte Carlo (MCMC) Gibbs sampler for Bayesian estimation of a hybrid choice model (HCM), using stated data on both vehicle purchase decisions and environmental concerns. Our study has two main contributions. The first is the feasibility of the Bayesian estimator we derive. Whereas classical estimation of HCMs is fairly complex, we show that the Bayesian approach for HCMs is methodologically easier to implement than simulated maximum likelihood because the inclusion of latent variables translates into adding independent ordinary regressions; we also find that, using the Bayesian estimates, forecasting and deriving confidence intervals for willingness to pay measures is straightforward. The second is the capacity of HCMs to adapt to practical situations. Our empirical results coincide with a priori expectations, namely that environmentally-conscious consumers are willing to pay more for low-emission vehicles. The model outperforms standard discrete choice models because it not only incorporates pro-environmental preferences but also provides tools to build a profile of environmentally-conscious consumers.

The Effect of Incentive Policies on the Practice Location of Doctors: A Multinomial Probit Analysis

In this article we estimate a spatial autoregressive multinomial probit model of the choice of location by general practitioners for establishing their initial practice. This model allows us to account for potential interdependencies among location choices. The model is used to assess the effect of various incentive measures introduced in Québec (Canada) to influence the geographical distribution of physicians across 18 regions. Our data set covers subperiods before and after the introduction of these measures. Incentive policies are captured through price and income effects. Our results provide evidence that these measures had a significant effect on location choices.

The choice of medical providers in rural Benin: A comparison of discrete choice models

In this paper we estimate three different discrete choice models of provider choice using data from the rural District of Ouidah in Bénin. These three model are: Multinomial Logit (ML); (2) Independent Multinomial Probit (IMP); (3) Multinomial Probit (MP). A comparison of IMP and MP allows us to reject the independence assumption between providers. Furthermore, the cross-price elasticities computed from the restrictive specifications (ML and IMP) are dramatically different from those computed from the more general one (MP). These results cast some doubt on the validity of the previous findings and policy recommendations that are typically based on the ML specification.

Hybrid Choice Modeling of New Technologies for Car Choice in Canada

In the past decade, a new trend in discrete choice modeling has emerged: psychological factors are explicitly incorporated to enhance the behavioral representation of the choice process. In this context, hybrid models expand on standard choice models by including attitudes and perceptions as latent variables. The complete model is composed of a group of structural equations describing the latent variables in terms of observable exogenous variables and a group of measurement relationships linking latent variables to certain observable indicators. Although the estimation of hybrid models requires the evaluation of complex multidimensional integrals, simulated maximum likelihood is implemented to solve the integrated multiple-equation model. This study empirically evaluates the application of hybrid choice modeling to data from a survey conducted by the Energy and Materials Research Group (Simon Fraser University, 2002 and 2003) of the virtual personal vehicle choices made by Canadian consumers when they are faced with technological innovations. The survey also includes a complete list of indicators that allows the application of a hybrid choice model formulation. It is concluded that the hybrid choice model is genuinely capable of adapting to practical situations by including latent variables among the set of explanatory variables. The incorporation of perceptions and attitudes in this way leads to more realistic models and gives a better description of the profile of consumers and their adoption of new automobile technologies.

Hybrid Choice Models with Logit Kernel: Applicability to Large Scale Models

The conceptual framework of the hybrid choice model (HCM) possesses many ingredients to enhance the behavioral representation of the choice process and therefore addresses the problem raised. As a direct result, the choice model specification is improved and it gains in predictive power. The application of HCMs have been limited to small scale models with two or three alternatives. The paper focuses on the major issue that arises from applying hybrid choice models with logit kernel to large scale problems. It concerns probability simulation of large dimensional integrals that arise from the inclusion of numerous attitudes and perceptions in models with large sets of potentially interrelated choices. The paper suggests simulation driven Bayesian and classical approaches to the econometric estimation of models with large number of dimension and flexible choice model formulations. Econometrically, the estimation of hybrid choice models can be extremely involved, but the progress in computer technologies now permits addressing those complicated problems.

Sequential and Simultaneous Estimation of Hybrid Discrete Choice Models: Some New Findings

The formulation of hybrid discrete choice models, including both observable alternative attributes and latent variables associated with attitudes and perceptions, has become a topic of discussion once more. To estimate models integrating both kinds of variables, two methods have been proposed: the sequential approach, in which the latent variables are built before their integration with the traditional explanatory variables in the choice model and the simultaneous approach, in which both processes are done together, albeit with a sophisticated but fairly complex treatment. Here both approaches are applied to estimate hybrid choice models by using two data sets: one from the Santiago Panel (an urban mode choice context with many alternatives) and another consisting of synthetic data. Differences between both approaches were found as well as similarities not found in earlier studies. Even when both approaches result in unbiased estimators, problems arise when valuations are obtained such as the value of time for forecasting and policy evaluation.

Multinomial Probit Estimation of Spatially Interdependent Choices: An Empirical Comparison of Two New Techniques

The paper compares the empirical performance of two recently suggested techniques for estimating Multinomial Probit (MNP) models. The application concerns the choice of the first practice location of general practitioners in Quebec (Canada). Regional similarities are accounted for by modeling interdependent choice decisions. One technique is a simulated maximum likelihood based approach that relies on a Geweke, Hajivassiliou, and Keane (GHK) choice probability simulator, and the other one exploits the Gibbs sampler with data augmentation. The results indicate that both estimation techniques give similar results. Compared to its competitor, the Gibbs approach is much simpler to implement both conceptually and computationally.

Modeling Methods for Discrete Choice Analysis

This paper introduces new forms, sampling and estimation approaches fordiscrete choice models. The new models include behavioral specifications oflatent class choice models, multinomial probit, hybrid logit, andnon-parametric methods. Recent contributions also include new specializedchoice based sample designs that permit greater efficiency in datacollection. Finally, the paper describes recent developments in the use ofsimulation methods for model estimation. These developments are designed toallow the applications of discrete choice models to a wider variety ofdiscrete choice problems.