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Discrete Choice Modeling for Transportation

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  • Brownstone, David
Abstract
This paper discusses important developments in discrete choice modeling for transportation applications. Since there have been a number of excellent recent surveys of the discrete choice literature aimed at transportation applications (see Bhat, 1997 and 2000a), this paper will concentrate on new developments and areas given less weight in recent surveys. Small and Winston (1999) give an excellent review of the transportation demand literature that includes many examples of how discrete choice models have been used in demand analysis.

Suggested Citation

  • Brownstone, David, 2001. "Discrete Choice Modeling for Transportation," University of California Transportation Center, Working Papers qt29v7d1pk, University of California Transportation Center.
    • Handle: RePEc:cdl:uctcwp:qt29v7d1pk
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    References listed on IDEAS

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    1. Brownstone, David & Golob, Thomas F. & Kazimi, Camilla, 1991. "Modeling non-ignorable attrition and measurement error in panel surveys: an application to travel demand modeling," University of California Transportation Center, Working Papers qt7sh4d67b, University of California Transportation Center.
    2. Imbens, Guido W, 1992. "An Efficient Method of Moments Estimator for Discrete Choice Models with Choice-Based Sampling," Econometrica, Econometric Society, vol. 60(5), pages 1187-1214, September.
    3. Keane, Michael P, 1994. "A Computationally Practical Simulation Estimator for Panel Data," Econometrica, Econometric Society, vol. 62(1), pages 95-116, January.
    4. Small, Kenneth A, 1987. "A Discrete Choice Model for Ordered Alternatives," Econometrica, Econometric Society, vol. 55(2), pages 409-424, March.
    5. Koop, Gary & Poirier, Dale J., 2004. "Bayesian variants of some classical semiparametric regression techniques," Journal of Econometrics, Elsevier, vol. 123(2), pages 259-282, December.
    6. Chamberlain, Gary, 1984. "Panel data," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 22, pages 1247-1318, Elsevier.
    7. McFadden, Daniel, 1989. "A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration," Econometrica, Econometric Society, vol. 57(5), pages 995-1026, September.
    8. Brownstone, David & Train, Kenneth, 1998. "Forecasting new product penetration with flexible substitution patterns," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 109-129, November.
    9. Hensher, David A., 0. "The sensitivity of the valuation of travel time savings to the specification of unobserved effects," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 37(2-3), pages 129-142, April.
    10. John Geweke, 1999. "Using simulation methods for bayesian econometric models: inference, development,and communication," Econometric Reviews, Taylor & Francis Journals, vol. 18(1), pages 1-73.
    11. Kenneth Train, "undated". "Simulation Methods for Probit and Related Models Based on Convenient Error Partitioning," Working Papers _009, University of California at Berkeley, Econometrics Laboratory Software Archive.
    12. John Calfee & Clifford Winston & Randolph Stempski, 2001. "Econometric Issues In Estimating Consumer Preferences From Stated Preference Data: A Case Study Of The Value Of Automobile Travel Time," The Review of Economics and Statistics, MIT Press, vol. 83(4), pages 699-707, November.
    13. Calfee, John & Winston, Clifford, 1998. "The value of automobile travel time: implications for congestion policy," Journal of Public Economics, Elsevier, vol. 69(1), pages 83-102, July.
    14. Brownstone, David & Bunch, David S. & Train, Kenneth, 2000. "Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 34(5), pages 315-338, June.
    15. Hajivassiliou, Vassilis & McFadden, Daniel & Ruud, Paul, 1996. "Simulation of multivariate normal rectangle probabilities and their derivatives theoretical and computational results," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 85-134.
    16. Geweke, John F. & Keane, Michael P. & Runkle, David E., 1997. "Statistical inference in the multinomial multiperiod probit model," Journal of Econometrics, Elsevier, vol. 80(1), pages 125-165, September.
    17. McCulloch, Robert & Rossi, Peter E., 1994. "An exact likelihood analysis of the multinomial probit model," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 207-240.
    18. John Geweke, 1999. "Using Simulation Methods for Bayesian Econometric Models," Computing in Economics and Finance 1999 832, Society for Computational Economics.
    19. Jeffrey M. Wooldridge, 1999. "Asymptotic Properties of Weighted M-Estimators for Variable Probability Samples," Econometrica, Econometric Society, vol. 67(6), pages 1385-1406, November.
    20. Lee, Lung-Fei, 1992. "On Efficiency of Methods of Simulated Moments and Maximum Simulated Likelihood Estimation of Discrete Response Models," Econometric Theory, Cambridge University Press, vol. 8(4), pages 518-552, December.
    21. Bhat, Chandra R., 1998. "Accommodating variations in responsiveness to level-of-service measures in travel mode choice modeling," Transportation Research Part A: Policy and Practice, Elsevier, vol. 32(7), pages 495-507, September.
    22. 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.
    23. Pakes, Ariel & Pollard, David, 1989. "Simulation and the Asymptotics of Optimization Estimators," Econometrica, Econometric Society, vol. 57(5), pages 1027-1057, September.
    24. Bunch, David S., 1988. "A comparison of algorithms for maximum likelihood estimation of choice models," Journal of Econometrics, Elsevier, vol. 38(1-2), pages 145-167.
    25. Lancaster, Tony, 1997. "Bayes WESML Posterior inference from choice-based samples," Journal of Econometrics, Elsevier, vol. 79(2), pages 291-303, August.
    26. Koop, Gary & Poirier, Dale J., 1993. "Bayesian analysis of logit models using natural conjugate priors," Journal of Econometrics, Elsevier, vol. 56(3), pages 323-340, April.
    27. Joel L. Horowitz & N. E. Savin, 2001. "Binary Response Models: Logits, Probits and Semiparametrics," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 43-56, Fall.
    28. Imbens, Guido W, 1992. "An Efficient Method of Moments Estimator for Discrete Choice Models with Choice-Based Sampling," Econometrica, Econometric Society, vol. 60(5), pages 1187-1214, September.
    29. Donald W. K. Andrews, 1999. "Estimation When a Parameter Is on a Boundary," Econometrica, Econometric Society, vol. 67(6), pages 1341-1384, November.
    30. Poirier, Dale J, 1988. "Frequentist and Subjectivist Perspectives on the Problems of Model Building in Economics," Journal of Economic Perspectives, American Economic Association, vol. 2(1), pages 121-144, Winter.
    31. Poirier, Dale J., 1996. "A Bayesian analysis of nested logit models," Journal of Econometrics, Elsevier, vol. 75(1), pages 163-181, November.
    32. Elrod, Terry & Keane, Michael, 1995. "A Factor-Analytic Probit Model for Representing the Market Structure in Panel Data," MPRA Paper 52434, University Library of Munich, Germany.
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    Cited by:

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    2. Mariel, Petr & Ayala, Amaya de & Hoyos, David & Abdullah, Sabah, 2013. "Selecting random parameters in discrete choice experiment for environmental valuation: A simulation experiment," Journal of choice modelling, Elsevier, vol. 7(C), pages 44-57.
    3. Ricardo A. Daziano & Luis Miranda-Moreno & Shahram Heydari, 2013. "Computational Bayesian Statistics in Transportation Modeling: From Road Safety Analysis to Discrete Choice," Transport Reviews, Taylor & Francis Journals, vol. 33(5), pages 570-592, September.
    4. Hoyos Ramos, David, 2010. "Using discrete choice experiments for environmental valuation," BILTOKI 1134-8984, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).
    5. Ivan Jeliazkov & Angela Vossmeyer, 2018. "The impact of estimation uncertainty on covariate effects in nonlinear models," Statistical Papers, Springer, vol. 59(3), pages 1031-1042, September.
    6. Hoyos, David & Mariel, Petr & Fernández-Macho, Javier, 2009. "The influence of cultural identity on the WTP to protect natural resources: Some empirical evidence," Ecological Economics, Elsevier, vol. 68(8-9), pages 2372-2381, June.
    7. Daziano, Ricardo A. & Achtnicht, Martin, 2014. "Accounting for uncertainty in willingness to pay for environmental benefits," Energy Economics, Elsevier, vol. 44(C), pages 166-177.
    8. Bhatta, Bharat P. & Larsen, Odd I., 2011. "Are intrazonal trips ignorable?," Transport Policy, Elsevier, vol. 18(1), pages 13-22, January.
    9. Haghani, Milad & Bliemer, Michiel C.J. & Hensher, David A., 2021. "The landscape of econometric discrete choice modelling research," Journal of choice modelling, Elsevier, vol. 40(C).
    10. Michael N.A. Mensah & Adusei Jumah, 2021. "Electronic Money and Consumer Spending Behaviour: Evidence from Ghana," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 11(3), pages 1-6.
    11. Marcucci, Edoardo & Gatta, Valerio, 2011. "Regional airport choice: Consumer behaviour and policy implications," Journal of Transport Geography, Elsevier, vol. 19(1), pages 70-84.
    12. Bhatta, Bharat P. & Larsen, Odd I., 2011. "Errors in variables in multinomial choice modeling: A simulation study applied to a multinomial logit model of travel mode choice," Transport Policy, Elsevier, vol. 18(2), pages 326-335, March.

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