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 variables, heterogeneity, and latent segmentation. Both progress and challanges related to the development of the hybrid choice model are presented.
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|Date of creation:||22 Mar 2002|
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|Note:||Financial support from the Deutsche Forschungsgemeinschaft, SFB 504, at the University of Mannheim, is gratefully acknowledged.|
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- Denis Bolduc & Bernard Fortin & Stephen Gordon, 1997.
"Multinomial Probit Estimation of Spatially Interdependent Choices: An Empirical Comparison of Two New Techniques,"
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- McFadden, Daniel L., 2000.
Nobel Prize in Economics documents
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- Dufour, Jean-Marie & Jasiak, Joann, 2001. "Finite Sample Limited Information Inference Methods for Structural Equations and Models with Generated Regressors," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 42(3), pages 815-43, August.
- Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
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