IDEAS home Printed from https://ideas.repec.org/p/wiw/wiwrsa/ersa05p77.html
   My bibliography  Save this paper

Specification Of A Model To Measure - The Value Of Travel Time Savings From Binomial Data

Author

Listed:
  • Mogens Fosgerau

Abstract

This paper develops a semiparametric methodology for the evaluation of the distribution of the value of travel time savings (VTTS) from binary choice data. Fosgerau (2004) deals with the case of just one time component. This paper extends to the case of several time components. The methodology is applied to a recent large dataset of about 2200 car drivers who undertook a series of stated choice experiments. The VTTS is a fundamental concept in transport economics, being the main yardstick against which transport investments are measured. However, the methodology presented is generally applicable to evaluation of willingness to pay from binary choice data. Current standard-of-practice methodology applies a mixing distribution to a binary choice model in order to take account of individual heterogeneity. While this is definitely progress, there remains the problem of deciding which mixing distribution to apply. This problem is avoided here by using a nonparametric distribution. For prediction of choices, the choice of mixing distribution may matter less but it is absolutely crucial for evaluating willingness to pay. Even so, it is rare to see a justification for the choice of mixing distribution. The paper tests a range of parametric distributions against the semiparametric alternative.

Suggested Citation

  • Mogens Fosgerau, 2005. "Specification Of A Model To Measure - The Value Of Travel Time Savings From Binomial Data," ERSA conference papers ersa05p77, European Regional Science Association.
  • Handle: RePEc:wiw:wiwrsa:ersa05p77
    as

    Download full text from publisher

    File URL: https://www-sre.wu.ac.at/ersa/ersaconfs/ersa05/papers/77.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Pagan,Adrian & Ullah,Aman, 1999. "Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9780521355643, November.
    2. 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.
    3. David A. Hensher, 2001. "Measurement of the Valuation of Travel Time Savings," Journal of Transport Economics and Policy, University of Bath, vol. 35(1), pages 71-98, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. DE BORGER, Bruno & FOSGERAU, Mogens, 2006. "Discrete choices and the trade off between money and time: Another test of the theory of reference-dependent preferences," Working Papers 2006034, University of Antwerp, Faculty of Business and Economics.
    2. De Borger, Bruno & Fosgerau, Mogens, 2007. "Discrete choices and the trade-off between money and time: A test of the theory of reference-dependent preferences," MPRA Paper 3904, University Library of Munich, Germany.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Lewbel, Arthur & Tang, Xun, 2015. "Identification and estimation of games with incomplete information using excluded regressors," Journal of Econometrics, Elsevier, vol. 189(1), pages 229-244.
    3. Fosgerau, Mogens, 2007. "Using nonparametrics to specify a model to measure the value of travel time," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(9), pages 842-856, November.
    4. repec:spo:wpmain:info:hdl:2441/7182 is not listed on IDEAS
    5. Harding, Don & Pagan, Adrian, 2011. "An Econometric Analysis of Some Models for Constructed Binary Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 86-95.
    6. David Fairris & Gurleen Popli & Eduardo Zepeda, 2008. "Minimum Wages and the Wage Structure in Mexico," Review of Social Economy, Taylor & Francis Journals, vol. 66(2), pages 181-208.
    7. Christian Bontemps & Thierry Magnac & Eric Maurin, 2012. "Set Identified Linear Models," Econometrica, Econometric Society, vol. 80(3), pages 1129-1155, May.
    8. Mohamed CHIKHI & Claude DIEBOLT, 2022. "Testing the weak form efficiency of the French ETF market with the LSTAR-ANLSTGARCH approach using a semiparametric estimation," Eastern Journal of European Studies, Centre for European Studies, Alexandru Ioan Cuza University, vol. 13, pages 228-253, June.
    9. Joseph G. Altonji & Rosa L. Matzkin, 2001. "Panel Data Estimators for Nonseparable Models with Endogenous Regressors," NBER Technical Working Papers 0267, National Bureau of Economic Research, Inc.
    10. Inanoglu, Hulusi & Jacobs, Michael, Jr. & Liu, Junrong & Sickles, Robin, 2015. "Analyzing Bank Efficiency: Are "Too-Big-to-Fail" Banks Efficient?," Working Papers 15-016, Rice University, Department of Economics.
    11. Joel L. Horowitz, 2012. "Nonparametric additive models," CeMMAP working papers CWP20/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    12. 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.
    13. Patrick Saart & Jiti Gao & Nam Hyun Kim, 2014. "Semiparametric methods in nonlinear time series analysis: a selective review," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(1), pages 141-169, March.
    14. Martins-Filho, Carlos & Yao, Feng & Torero, Maximo, 2018. "Nonparametric Estimation Of Conditional Value-At-Risk And Expected Shortfall Based On Extreme Value Theory," Econometric Theory, Cambridge University Press, vol. 34(1), pages 23-67, February.
    15. Huang, Bai & Lee, Tae-Hwy & Ullah, Aman, 2020. "Combined estimation of semiparametric panel data models," Econometrics and Statistics, Elsevier, vol. 15(C), pages 30-45.
    16. Florencia Gabrielli, 2014. "Econometrics of First Price Auctions: a Survey of the Theoretical and Applied Literature," Económica, Departamento de Economía, Facultad de Ciencias Económicas, Universidad Nacional de La Plata, vol. 60, pages 77-118, January-D.
    17. Martínez-Iriarte, Julián & Montes-Rojas, Gabriel & Sun, Yixiao, 2024. "Unconditional effects of general policy interventions," Journal of Econometrics, Elsevier, vol. 238(2).
    18. McMillen, Daniel P. & Smith, Stefani C., 2003. "The number of subcenters in large urban areas," Journal of Urban Economics, Elsevier, vol. 53(3), pages 321-338, May.
    19. Connor, Gregory & Linton, Oliver, 2007. "Semiparametric estimation of a characteristic-based factor model of common stock returns," Journal of Empirical Finance, Elsevier, vol. 14(5), pages 694-717, December.
    20. Clifton, Geoffrey T. & Mulley, Corinne, 2016. "A historical overview of enhanced bus services in Australian cities: What has been tried, what has worked?," Research in Transportation Economics, Elsevier, vol. 59(C), pages 11-25.
    21. Malmendier, Ulrike M. & Della Vigna, Stefano, 2002. "Overestimating Self-Control: Evidence from the Health Club Industry," Research Papers 1880, Stanford University, Graduate School of Business.

    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric 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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wiw:wiwrsa:ersa05p77. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Gunther Maier (email available below). General contact details of provider: http://www.ersa.org .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.