IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/3877.html
   My bibliography  Save this paper

The Properties of Market-Based and Survey Forecasts for Different Data Releases

Author

Listed:
  • Lanne, Markku

Abstract

We compare the accuracy of the survey forecasts and forecasts implied by economic binary options on the U.S. nonfarm payroll change. These options are available for a number of ranges of the announced figure, and each pays $1 if the released nonfarm payroll change falls in the given range. For the first-release data both the market-based and survey forecasts are biased, while they are rational and approximately equally accurate for later releases. Both forecasts are more accurate for later releases. Because of predictability in the revision process, this indicates that the investors in the economic derivatives market are incapable of taking the measurement error in the preliminary estimates efficiently into account. This suggests that economic stability could be enhanced by more accurate first-release figures.

Suggested Citation

  • Lanne, Markku, 2007. "The Properties of Market-Based and Survey Forecasts for Different Data Releases," MPRA Paper 3877, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:3877
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/3877/1/MPRA_paper_3877.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ottaviani, Marco & Sorensen, Peter Norman, 2006. "The strategy of professional forecasting," Journal of Financial Economics, Elsevier, vol. 81(2), pages 441-466, August.
    2. 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.
    3. Refet Gürkaynak & Justin Wolfers, 2005. "Macroeconomic Derivatives: An Initial Analysis of Market-Based Macro Forecasts, Uncertainty, and Risk," NBER Chapters, in: NBER International Seminar on Macroeconomics 2005, pages 11-50, National Bureau of Economic Research, Inc.
    4. Fair, Ray C & Shiller, Robert J, 1990. "Comparing Information in Forecasts from Econometric Models," American Economic Review, American Economic Association, vol. 80(3), pages 375-389, June.
    5. Bomfim, Antulio N., 2001. "Measurement error in general equilibrium: the aggregate effects of noisy economic indicators," Journal of Monetary Economics, Elsevier, vol. 48(3), pages 585-603, December.
    Full references (including those not matched with items on IDEAS)

    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. Markku Lanne, 2009. "Properties of Market-Based and Survey Macroeconomic Forecasts for Different Data Releases," Economics Bulletin, AccessEcon, vol. 29(3), pages 2231-2240.
    2. Refet Gürkaynak & Justin Wolfers, 2005. "Macroeconomic Derivatives: An Initial Analysis of Market-Based Macro Forecasts, Uncertainty, and Risk," NBER Chapters, in: NBER International Seminar on Macroeconomics 2005, pages 11-50, National Bureau of Economic Research, Inc.
    3. Clements, Michael P., 2006. "Internal consistency of survey respondentsíforecasts: Evidence based on the Survey of Professional Forecasters," Economic Research Papers 269742, University of Warwick - Department of Economics.
    4. Dueker, Michael J. & Sola, Martin & Spagnolo, Fabio, 2007. "Contemporaneous threshold autoregressive models: Estimation, testing and forecasting," Journal of Econometrics, Elsevier, vol. 141(2), pages 517-547, December.
    5. Snowberg, Erik & Wolfers, Justin & Zitzewitz, Eric, 2013. "Prediction Markets for Economic Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 657-687, Elsevier.
    6. Capistrán, Carlos, 2008. "Bias in Federal Reserve inflation forecasts: Is the Federal Reserve irrational or just cautious?," Journal of Monetary Economics, Elsevier, vol. 55(8), pages 1415-1427, November.
    7. Clements, Michael P., 2010. "Explanations of the inconsistencies in survey respondents' forecasts," European Economic Review, Elsevier, vol. 54(4), pages 536-549, May.
    8. repec:lan:wpaper:413 is not listed on IDEAS
    9. repec:lan:wpaper:470 is not listed on IDEAS
    10. McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
    11. repec:lan:wpaper:425 is not listed on IDEAS
    12. repec:lan:wpaper:539557 is not listed on IDEAS
    13. Taylor, James W., 2020. "A strategic predictive distribution for tests of probabilistic calibration," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1380-1388.
    14. Zacharias Psaradakis & Fabio Spagnolo, 2005. "Forecast performance of nonlinear error-correction models with multiple regimes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(2), pages 119-138.
    15. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    16. Christoffersen, Peter & Ghysels, Eric & Swanson, Norman R., 2002. "Let's get "real" about using economic data," Journal of Empirical Finance, Elsevier, vol. 9(3), pages 343-360, August.
    17. González-Rivera, Gloria & Sun, Yingying, 2017. "Density forecast evaluation in unstable environments," International Journal of Forecasting, Elsevier, vol. 33(2), pages 416-432.
    18. Berna Karali & Scott H. Irwin & Olga Isengildina‐Massa, 2020. "Supply Fundamentals and Grain Futures Price Movements," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(2), pages 548-568, March.
    19. Bedri Kamil Onur Taş, 2016. "Does the Federal Reserve have Private Information about its Future Actions?," Economica, London School of Economics and Political Science, vol. 83(331), pages 498-517, July.
    20. Wolfgang Polasek, 2013. "Forecast Evaluations for Multiple Time Series: A Generalized Theil Decomposition," Working Paper series 23_13, Rimini Centre for Economic Analysis.
    21. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    22. Corradi, Valentina & Swanson, Norman R., 2004. "A test for the distributional comparison of simulated and historical data," Economics Letters, Elsevier, vol. 85(2), pages 185-193, November.
    23. Mehmet Balcilar & Rangan Gupta & Stephen M. Miller, 2015. "The out-of-sample forecasting performance of nonlinear models of regional housing prices in the US," Applied Economics, Taylor & Francis Journals, vol. 47(22), pages 2259-2277, May.
    24. Martinez-Martin Jaime & Morris Richard & Onorante Luca & Piersanti Fabio Massimo, 2024. "Merging Structural and Reduced-Form Models for Forecasting," The B.E. Journal of Macroeconomics, De Gruyter, vol. 24(1), pages 399-437, January.

    More about this item

    Keywords

    Expectations; economic derivatives; data vintage; real-time data;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access

    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:3877. 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.