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Mixed Logit Estimation of the Value of Travel Time

Author

Listed:
  • Algers, Staffan

    (Royal Institute of Technology)

  • Bergström, Pål

    (Trade och Capital Markets)

  • Dahlberg, Matz

    (Department of Economics)

  • Lindqvist Dillén, Johanna

    (Royal Institute of Technology, Stockholm)

Abstract

In this paper we use mixed logit specifications to allow parameters to vary in the population when estimating the value of time for long-distance car travel. Our main conclusion is that the estimated value of time is very sensitive to how the model is specified: we find that it is significantly lower when the coefficients are assumed to be normally distributed in the population, as compared to the traditional case when they are treated as fixed. In our most richly parameterised model, we find a median value of time of 57 SEK per hour, with the major part of the mass of the value of time distribution closely centred around the median value. The corresponding figure when the parameters are treated as fixed is 89 SEK per hour. Furthermore, our finding that the ratio of coefficients in a mixed logit specification differ significantly from the ones in a traditional logit specification is contrary to the results obtained by Brownstone & Train (1996) and Train (1997). Whether the ratios will differ or not depends on the model and the data generating process at hand.

Suggested Citation

  • Algers, Staffan & Bergström, Pål & Dahlberg, Matz & Lindqvist Dillén, Johanna, 1998. "Mixed Logit Estimation of the Value of Travel Time," Working Paper Series 1998:15, Uppsala University, Department of Economics.
    • Handle: RePEc:hhs:uunewp:1998_015
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    References listed on IDEAS

    as
    1. Axel Borsch-Supan & Vassilis Hajivassiliou & Laurence J. Kotlikoff, 1992. "Health, Children, and Elderly Living Arrangements: A Multiperiod-Multinomial Probit Model with Unobserved Heterogeneity and Autocorrelated Errors," NBER Chapters, in: Topics in the Economics of Aging, pages 79-108, National Bureau of Economic Research, Inc.
    2. McFadden, Daniel & Ruud, Paul A, 1994. "Estimation by Simulation," The Review of Economics and Statistics, MIT Press, vol. 76(4), pages 591-608, November.
    3. Keane, Michael P, 1994. "A Computationally Practical Simulation Estimator for Panel Data," Econometrica, Econometric Society, vol. 62(1), pages 95-116, January.
    4. Brownstone, David & Train, Kenneth, 1999. "Forecasting new product penetration with flexible substitution patterns," Department of Economics, Working Paper Series qt3tb6j874, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    5. Hajivassiliou, Vassilis A. & Ruud, Paul A., 1986. "Classical estimation methods for LDV models using simulation," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 40, pages 2383-2441, Elsevier.
    6. Borsch-Supan, Axel & Hajivassiliou, Vassilis A., 1993. "Smooth unbiased multivariate probability simulators for maximum likelihood estimation of limited dependent variable models," Journal of Econometrics, Elsevier, vol. 58(3), pages 347-368, August.
    7. 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.
    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. 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.
    10. 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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    More about this item

    Keywords

    Mixed Logit; Simulation Estimation; Value of Time;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise

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