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A discrete choice model incorporating thresholds for perception in attribute values

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  • Cantillo, Víctor
  • Heydecker, Benjamin
  • de Dios Ortúzar, Juan
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    Abstract

    In this paper we formulate a discrete choice model that incorporates thresholds in the perception of changes in attribute values. The model considers multiple options and allows for changes in several attributes. We postulate that if thresholds exist they could be random, differ between individuals, and even be a function of socio-economic characteristics and choice conditions. Our formulation allows estimation of the parameters of the threshold probability distribution starting from information about choices. The model is applied to synthetic data and also to real data from a stated preference survey. We found that where perception thresholds exist in the population, the use of models without them leads to errors in estimation and prediction. Clearly, the effect is more relevant when the typical size of change in the attribute value is comparable with the threshold, and when the contribution of this attribute in the utility function is substantial. Finally, we discuss the implications of the threshold model for estimation of the benefits of transport investments, and show that where thresholds exist, models that do not represent them can overestimate benefits substantially.

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    Bibliographic Info

    Article provided by Elsevier in its journal Transportation Research Part B: Methodological.

    Volume (Year): 40 (2006)
    Issue (Month): 9 (November)
    Pages: 807-825

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    Handle: RePEc:eee:transb:v:40:y:2006:i:9:p:807-825

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    References

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    1. Caussade, Sebastián & Ortúzar, Juan de Dios & Rizzi, Luis I. & Hensher, David A., 2005. "Assessing the influence of design dimensions on stated choice experiment estimates," Transportation Research Part B: Methodological, Elsevier, vol. 39(7), pages 621-640, August.
    2. Timothy J. Gilbride & Greg M. Allenby, 2004. "A Choice Model with Conjunctive, Disjunctive, and Compensatory Screening Rules," Marketing Science, INFORMS, vol. 23(3), pages 391-406, October.
    3. Kenneth Train, 2003. "Discrete Choice Methods with Simulation," Online economics textbooks, SUNY-Oswego, Department of Economics, number emetr2, January.
    4. K. S. Krishnan, 1977. "Incorporating Thresholds of Indifference in Probabilistic Choice Models," Management Science, INFORMS, vol. 23(11), pages 1224-1233, July.
    5. Li, Chin-Shang & Hunt, Daniel, 2004. "Regression splines for threshold selection with application to a random-effects logistic dose-response model," Computational Statistics & Data Analysis, Elsevier, vol. 46(1), pages 1-9, May.
    6. Swait, Joffre, 2001. "A non-compensatory choice model incorporating attribute cutoffs," Transportation Research Part B: Methodological, Elsevier, vol. 35(10), pages 903-928, November.
    7. Kitamura, Ryuichi, 1990. "Panel Analysis in Transportation Planning: An Overview," University of California Transportation Center, Working Papers qt86v0f7zh, University of California Transportation Center.
    8. Munizaga, Marcela A. & Heydecker, Benjamin G. & Ortúzar, Juan de Dios, 2000. "Representation of heteroskedasticity in discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 34(3), pages 219-240, April.
    9. Cantillo, Víctor & Ortúzar, Juan de Dios, 2005. "A semi-compensatory discrete choice model with explicit attribute thresholds of perception," Transportation Research Part B: Methodological, Elsevier, vol. 39(7), pages 641-657, August.
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