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An approach to the estimation of the distribution of marginal valuations from discrete choice data

Author

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  • Fosgerau, Mogens
  • Hjort, Katrine
  • Vincent Lyk-Jensen, Stéphanie

Abstract

Models such as the mixed logit are often used to measure the distribution of the marginal value of a good based on discrete choice panel data. There are however serious specification and identification issues that are rarely addressed. The consequences for results may be dramatic. This paper points out the issues and presents an approach to dealing with them that may be applied under some circumstances. The issues and the approach are illustrated using a dataset designed to measure the value of travel time.

Suggested Citation

  • Fosgerau, Mogens & Hjort, Katrine & Vincent Lyk-Jensen, Stéphanie, 2007. "An approach to the estimation of the distribution of marginal valuations from discrete choice data," MPRA Paper 3907, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:3907
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    File URL: https://mpra.ub.uni-muenchen.de/3907/1/MPRA_paper_3907.pdf
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    References listed on IDEAS

    as
    1. Train, K. & Weeks, M., 2004. "Discrete Choice Models in Preference Space and Willingness-to Pay Space," Cambridge Working Papers in Economics 0443, Faculty of Economics, University of Cambridge.
    2. Ian Bateman & Alistair Munro & Bruce Rhodes & Chris Starmer & Robert Sugden, 1997. "A Test of the Theory of Reference-Dependent Preferences," The Quarterly Journal of Economics, Oxford University Press, vol. 112(2), pages 479-505.
    3. Lewbel, Arthur & McFadden, Daniel & Linton, Oliver, 2011. "Estimating features of a distribution from binomial data," Journal of Econometrics, Elsevier, vol. 162(2), pages 170-188, June.
    4. Cameron, Trudy Ann, 1988. "A new paradigm for valuing non-market goods using referendum data: Maximum likelihood estimation by censored logistic regression," Journal of Environmental Economics and Management, Elsevier, vol. 15(3), pages 355-379, September.
    5. 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.
    6. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387.
    7. Fosgerau, Mogens, 2006. "Investigating the distribution of the value of travel time savings," Transportation Research Part B: Methodological, Elsevier, vol. 40(8), pages 688-707, September.
    8. Mogens Fosgerau & Michel Bierlaire, 2005. "A practical test for the choice of mixing distribution in a discrete choice model," Econometrics 0512002, EconWPA.
    9. Bierens, Herman J., 2008. "Semi-Nonparametric Interval-Censored Mixed Proportional Hazard Models: Identification And Consistency Results," Econometric Theory, Cambridge University Press, vol. 24(03), pages 749-794, June.
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    Citations

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    Cited by:

    1. Ojeda-Cabral, Manuel & Chorus, Caspar G., 2016. "Value of travel time changes: Theory and simulation to understand the connection between Random Valuation and Random Utility methods," Transport Policy, Elsevier, vol. 48(C), pages 139-145.
    2. Fosgerau, Mogens & Nielsen, Søren Feodor, 2010. "Deconvoluting Preferences And Errors: A Model For Binomial Panel Data," Econometric Theory, Cambridge University Press, vol. 26(06), pages 1846-1854, December.
    3. Hess, Stephane & Daly, Andrew & Dekker, Thijs & Cabral, Manuel Ojeda & Batley, Richard, 2017. "A framework for capturing heterogeneity, heteroskedasticity, non-linearity, reference dependence and design artefacts in value of time research," Transportation Research Part B: Methodological, Elsevier, vol. 96(C), pages 126-149.

    More about this item

    Keywords

    Discrete choice; valuation; mixed logit;

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • Q51 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Valuation of Environmental Effects

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