IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/12861.html
   My bibliography  Save this paper

Bayesian posterior prediction and meta-analysis: an application to the value of travel time savings

Author

Listed:
  • Moral-Benito, Enrique

Abstract

In the evaluation of transportation infrastructure projects, some non-tradable goods such as time are usually key determinants of the result. However, obtaining monetary values for these goods is not always easy. This paper introduces a novel approach based on the combination of bayesian posterior prediction and meta-analysis. This methodology will allow to obtain predictive distributions of the monetary values for this type of goods. Therefore, uncertainty is formally considered in the analysis. Moreover, the proposed method is easy to apply and inexpensive both in terms of time and money. Finally, an application to the value of travel time savings is also presented.

Suggested Citation

  • Moral-Benito, Enrique, 2008. "Bayesian posterior prediction and meta-analysis: an application to the value of travel time savings," MPRA Paper 12861, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:12861
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/12861/1/MPRA_paper_12861.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Luca Zamparini & Aura Reggiani, 2007. "Meta-Analysis and the Value of Travel Time Savings: A Transatlantic Perspective in Passenger Transport," Networks and Spatial Economics, Springer, vol. 7(4), pages 377-396, December.
    2. Viscusi, W Kip & Aldy, Joseph E, 2003. "The Value of a Statistical Life: A Critical Review of Market Estimates throughout the World," Journal of Risk and Uncertainty, Springer, vol. 27(1), pages 5-76, August.
    3. Francis X. Diebold & Jose A. Lopez, 1995. "Forecast evaluation and combination," Research Paper 9525, Federal Reserve Bank of New York.
    4. Campos, Javier & de Rus, Gines & Barron, Ignacio, 2007. "A review of HSR experiences around the world," MPRA Paper 12397, University Library of Munich, Germany.
    5. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-883, November.
    6. repec:reg:rpubli:282 is not listed on IDEAS
    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. Geyer-Klingeberg, Jerome & Hang, Markus & Rathgeber, Andreas, 2020. "Meta-analysis in finance research: Opportunities, challenges, and contemporary applications," International Review of Financial Analysis, Elsevier, vol. 71(C).

    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. M. Hashem Pesaran & Paolo Zaffaroni, 2004. "Model Averaging and Value-at-Risk Based Evaluation of Large Multi Asset Volatility Models for Risk Management," CESifo Working Paper Series 1358, CESifo.
    2. Jose A. Lopez, 1999. "Methods for evaluating value-at-risk estimates," Economic Review, Federal Reserve Bank of San Francisco, pages 3-17.
    3. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.
    4. repec:lan:wpaper:470 is not listed on IDEAS
    5. Cees Diks & Valentyn Panchenko & Dick van Dijk, 2008. "Partial Likelihood-Based Scoring Rules for Evaluating Density Forecasts in Tails," Tinbergen Institute Discussion Papers 08-050/4, Tinbergen Institute.
    6. Diks, Cees & Panchenko, Valentyn & van Dijk, Dick, 2011. "Likelihood-based scoring rules for comparing density forecasts in tails," Journal of Econometrics, Elsevier, vol. 163(2), pages 215-230, August.
    7. Lucio Sarno, 2003. "Nonlinear Exchange Rate Models: A Selective Overview," Rivista di Politica Economica, SIPI Spa, vol. 93(4), pages 3-46, July-Augu.
    8. Elliott, Graham & Timmermann, Allan, 2004. "Optimal forecast combinations under general loss functions and forecast error distributions," Journal of Econometrics, Elsevier, vol. 122(1), pages 47-79, September.
    9. Polanski, Arnold & Stoja, Evarist, 2012. "Efficient evaluation of multidimensional time-varying density forecasts, with applications to risk management," International Journal of Forecasting, Elsevier, vol. 28(2), pages 343-352.
    10. Francis X. Diebold & Jinyong Hahn & Anthony S. Tay, 1998. "Real-Time Multivariate Density Forecast Evaluation and Calibration: Monitoring the Risk of High-Frequency Returns on Foreign Exchange," New York University, Leonard N. Stern School Finance Department Working Paper Seires 98-079, New York University, Leonard N. Stern School of Business-.
    11. Garratt A. & Lee K. & Pesaran M.H. & Shin Y., 2003. "Forecast Uncertainties in Macroeconomic Modeling: An Application to the U.K. Economy," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 829-838, January.
    12. Pesaran, M. Hashem & Schleicher, Christoph & Zaffaroni, Paolo, 2009. "Model averaging in risk management with an application to futures markets," Journal of Empirical Finance, Elsevier, vol. 16(2), pages 280-305, March.
    13. Rodríguez, Gabriel, 2017. "Modeling Latin-American stock and Forex markets volatility: Empirical application of a model with random level shifts and genuine long memory," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 393-420.
    14. Diks, Cees & Panchenko, Valentyn & Sokolinskiy, Oleg & van Dijk, Dick, 2014. "Comparing the accuracy of multivariate density forecasts in selected regions of the copula support," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 79-94.
    15. Lopez, Jose A. & Saidenberg, Marc R., 2000. "Evaluating credit risk models," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 151-165, January.
    16. Giorgio Valente & Lucio Sarno, 2004. "Comparing the accuracy of density forecasts from competing models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(8), pages 541-557.
    17. Jose A. Lopez & Christian Walter, 2000. "Evaluating covariance matrix forecasts in a value-at-risk framework," Working Paper Series 2000-21, Federal Reserve Bank of San Francisco.
    18. Cees Diks & Valentyn Panchenko & Oleg Sokolinskiy, & Dick van Dijk, 2013. "Comparing the Accuracy of Copula-Based Multivariate Density Forecasts in Selected Regions of Support," Tinbergen Institute Discussion Papers 13-061/III, Tinbergen Institute.
    19. Michael Clements, 2006. "Evaluating the survey of professional forecasters probability distributions of expected inflation based on derived event probability forecasts," Empirical Economics, Springer, vol. 31(1), pages 49-64, March.
    20. Amisano, Gianni & Giacomini, Raffaella, 2007. "Comparing Density Forecasts via Weighted Likelihood Ratio Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 177-190, April.
    21. Casey, Eddie, 2021. "Are professional forecasters overconfident?," International Journal of Forecasting, Elsevier, vol. 37(2), pages 716-732.

    More about this item

    Keywords

    Bayesian Prediction; Meta-Analysis; Uncertainty; Value of Travel Time Savings;
    All these keywords.

    JEL classification:

    • L91 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Transportation: General
    • D61 - Microeconomics - - Welfare Economics - - - Allocative Efficiency; Cost-Benefit Analysis

    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:pra:mprapa:12861. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    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.