IDEAS home Printed from https://ideas.repec.org/p/cwl/cwldpp/774.html
   My bibliography  Save this paper

Forecasting Efficiency: Concepts and Applications

Author

Abstract

The question of forecasting accuracy is, of course, one that has been the subject of numerous investigations over the last two decades. The present study contributes to this line of research in two ways. First, we introduce a new concept, called "forecast efficiency," that measures the extent to which information is incorporated into forecasts. This concept is closely related to concepts of efficiency used in the analysis of stock and other financial markets. The paper proves two readily testable propositions about efficient forecasts. Second, the empirical part of the study examines forecast efficiency by looking at forecast revisions ("fixed-horizon forecasts"), rather than a series of forecasts of different events ("rolling-horizon forecasts") as is the case for most studies of forecasting. This new approach to estimation in certain circumstances will provide a more powerful test of forecast efficiency. A number of fixed-horizon forecasts are collected and these are tested for forecast efficiency.

Suggested Citation

  • William D. Nordhaus, 1985. "Forecasting Efficiency: Concepts and Applications," Cowles Foundation Discussion Papers 774, Cowles Foundation for Research in Economics, Yale University.
  • Handle: RePEc:cwl:cwldpp:774
    Note: CFP 692.
    as

    Download full text from publisher

    File URL: https://cowles.yale.edu/sites/default/files/files/pub/d07/d0774.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Robert J. Shiller, 1984. "Stock Prices and Social Dynamics," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 15(2), pages 457-510.
    2. Arrow, Kenneth J, 1982. "Risk Perception in Psychology and Economics," Economic Inquiry, Western Economic Association International, vol. 20(1), pages 1-9, January.
    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. Bruno S. Frey & Reiner Eichenberger, 1989. "Should Social Scientists Care about Choice Anomalies?," Rationality and Society, , vol. 1(1), pages 101-122, July.
    2. Amrei Lahno & Marta Serra-Garcia, 2015. "Peer effects in risk taking: Envy or conformity?," Journal of Risk and Uncertainty, Springer, vol. 50(1), pages 73-95, February.
    3. Dindo, Pietro & Massari, Filippo, 2020. "The wisdom of the crowd in dynamic economies," Theoretical Economics, Econometric Society, vol. 15(4), November.
    4. Juann H. Hung, 1995. "Intervention strategies and exchange rate volatility: a noise trading perspective," Research Paper 9515, Federal Reserve Bank of New York.
    5. Cakici, Nusret & Zaremba, Adam, 2022. "Salience theory and the cross-section of stock returns: International and further evidence," Journal of Financial Economics, Elsevier, vol. 146(2), pages 689-725.
    6. Kumar, Alok, 2007. "Do the diversification choices of individual investors influence stock returns?," Journal of Financial Markets, Elsevier, vol. 10(4), pages 362-390, November.
    7. De Long, J Bradford & Shleifer, Andrei & Summers, Lawrence H & Waldmann, Robert J, 1991. "The Survival of Noise Traders in Financial Markets," The Journal of Business, University of Chicago Press, vol. 64(1), pages 1-19, January.
    8. Robert S. Chirinko & Edward P. Harper, 1993. "Buckle up or slow down? New estimates of offsetting behavior and their implications for automobile safety regulation," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 12(2), pages 270-296.
    9. Tsai, I-Chun & Chiang, Shu-Hen, 2019. "Exuberance and spillovers in housing markets: Evidence from first- and second-tier cities in China," Regional Science and Urban Economics, Elsevier, vol. 77(C), pages 75-86.
    10. Botzen, W.J. Wouter & Marey, Philip S., 2010. "Did the ECB respond to the stock market before the crisis?," Journal of Policy Modeling, Elsevier, vol. 32(3), pages 303-322, May.
    11. Lahno, Amrei M. & Serra-Garcia, Marta, 2012. "Peer Effects in Risk Taking," Discussion Papers in Economics 14309, University of Munich, Department of Economics.
    12. Corgnet, Brice & Hernán-González, Roberto & Kujal, Praveen, 2020. "On booms that never bust: Ambiguity in experimental asset markets with bubbles," Journal of Economic Dynamics and Control, Elsevier, vol. 110(C).
    13. Alexander S. Sangare, 2005. "Efficience des marchés : un siècle après Bachelier," Revue d'Économie Financière, Programme National Persée, vol. 81(4), pages 107-132.
    14. De Long, J Bradford & Andrei Shleifer & Lawrence H. Summers & Robert J. Waldmann, 1990. "Noise Trader Risk in Financial Markets," Journal of Political Economy, University of Chicago Press, vol. 98(4), pages 703-738, August.
    15. Delcoure, Natalya & Zhong, Maosen, 2007. "On the premiums of iShares," Journal of Empirical Finance, Elsevier, vol. 14(2), pages 168-195, March.
    16. Wagner, Moritz & Lee, John Byong-Tek & Margaritis, Dimitris, 2022. "Mutual fund flows and seasonalities in stock returns," Journal of Banking & Finance, Elsevier, vol. 144(C).
    17. Andrey Kudryavtsev, 2014. "When Do Opening Stock Returns Tend to be Higher?," International Economic Journal, Taylor & Francis Journals, vol. 28(3), pages 445-458, September.
    18. A. Corcos & J-P Eckmann & A. Malaspinas & Y. Malevergne & D. Sornette, 2002. "Imitation and contrarian behaviour: hyperbolic bubbles, crashes and chaos," Quantitative Finance, Taylor & Francis Journals, vol. 2(4), pages 264-281.
    19. Jiali Liu & Xinran Xie & Yu Duan & Liang Tang, 2023. "Peer effects and the mechanisms in corporate capital structure: evidence from Chinese listed firms," Oeconomia Copernicana, Institute of Economic Research, vol. 14(1), pages 295-326, March.
    20. Klein, A. & Urbig, D. & Kirn, S., 2008. "Who Drives the Market? Estimating a Heterogeneous Agent-based Financial Market Model Using a Neural Network Approach," MPRA Paper 14433, University Library of Munich, Germany.

    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:cwl:cwldpp:774. 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: Brittany Ladd (email available below). General contact details of provider: https://edirc.repec.org/data/cowleus.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.