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Choice set generation within the generalized extreme value family of discrete choice models

  • Swait, Joffre

This paper introduces a new member of the generalized extreme value (GEV) family of discrete choice models that directly incorporates choice set generation modeling into the specification via the GEV generating function. Though still a two-stage model of choice set generation and choice, the proposed model specifies choice set generation endogenously and directly reflective of preferences, which further differentiates it from extant models of choice set formation. The properties of the model, denominated GenL (choice set Generation Logit), are examined in detail. A case study involving intercity mode choice by non-business travelers is presented to illustrate model estimation and interpretation, as well as to obtain insights into possible data generation process characteristics that lead to violation of GEV conditions for the model.

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

Volume (Year): 35 (2001)
Issue (Month): 7 (August)
Pages: 643-666

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Handle: RePEc:eee:transb:v:35:y:2001:i:7:p:643-666
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  1. Koppelman, Frank S. & Wen, Chieh-Hua, 2000. "The paired combinatorial logit model: properties, estimation and application," Transportation Research Part B: Methodological, Elsevier, vol. 34(2), pages 75-89, February.
  2. Gaundry, Marc J. I. & Dagenais, Marcel G., 1979. "The dogit model," Transportation Research Part B: Methodological, Elsevier, vol. 13(2), pages 105-111, June.
  3. Brownstone, David & Train, Kenneth, 1999. "Forecasting new product penetration with flexible substitution patterns," University of California Transportation Center, Working Papers qt3tb6j874, University of California Transportation Center.
  4. Swait, Joffre, 2001. "A non-compensatory choice model incorporating attribute cutoffs," Transportation Research Part B: Methodological, Elsevier, vol. 35(10), pages 903-928, November.
  5. Small, Kenneth A., 1994. "Approximate generalized extreme value models of discrete choice," Journal of Econometrics, Elsevier, vol. 62(2), pages 351-382, June.
  6. Swait, Joffre & Ben-Akiva, Moshe, 1987. "Empirical test of a constrained choice discrete model: Mode choice in São Paulo, Brazil," Transportation Research Part B: Methodological, Elsevier, vol. 21(2), pages 103-115, April.
  7. Herriges, Joseph A. & Kling, Catherine L., 1996. "Testing the Consistency of Nested Logit Models with Utility Maximization," Staff General Research Papers 1500, Iowa State University, Department of Economics.
  8. Borsch-Supan, Axel, 1990. "On the compatibility of nested logit models with utility maximization," Journal of Econometrics, Elsevier, vol. 43(3), pages 373-388, March.
  9. H C W L Williams, 1977. "On the Formation of Travel Demand Models and Economic Evaluation Measures of User Benefit," Environment and Planning A, , vol. 9(3), pages 285-344, March.
  10. Small, Kenneth A, 1987. "A Discrete Choice Model for Ordered Alternatives," Econometrica, Econometric Society, vol. 55(2), pages 409-24, March.
  11. Klein, Noreen M & Bither, Stewart W, 1987. " An Investigation of Utility-Directed Cutoff Selection," Journal of Consumer Research, University of Chicago Press, vol. 14(2), pages 240-56, September.
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