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Predicting events with an unidentified time horizon

Author

Listed:
  • Patrick De lamirande

    () (CBU)

  • Jason Stevens

    () (University of Prince Edward Island)

Abstract

Economists often employ binary choice models to determine if variables of interest such as asset prices or returns are able to predict the occurrence of significant events, most notably recessions. It is, however, unclear how the results of existing studies should be interpreted due to the common practice of testing the predictability of the event at multiple horizons. Presented with a set of test statistics, some may be tempted to conclude that the variable of interest is able to predict the event if the null hypothesis of non-predictability is rejected at any horizon. This paper demonstrates that this approach results in a significant probability of spuriously concluding that the event of interest is predictable. In light of this possibility, the ability of the term spread to predict US recessions is re-examined with corrected critical values, confirming that the results found in the existing literature are not the result of data-snooping.

Suggested Citation

  • Patrick De lamirande & Jason Stevens, 2016. "Predicting events with an unidentified time horizon," Economics Bulletin, AccessEcon, vol. 36(2), pages 729-735.
  • Handle: RePEc:ebl:ecbull:eb-14-00779
    as

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    References listed on IDEAS

    as
    1. Fabio Moneta, 2005. "Does the Yield Spread Predict Recessions in the Euro Area?," International Finance, Wiley Blackwell, vol. 8(2), pages 263-301, August.
    2. Chow, K. Victor & Denning, Karen C., 1993. "A simple multiple variance ratio test," Journal of Econometrics, Elsevier, vol. 58(3), pages 385-401, August.
    3. Atta-Mensah, Joseph & Tkacz, Greg, 1998. "Predicting Canadian Recessions Using Financial Variables: A Probit Approach," Staff Working Papers 98-5, Bank of Canada.
    4. Norbert Funke, 1997. "Predicting recessions: Some evidence for Germany," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 133(1), pages 90-102, March.
    5. Marcelle Chauvet & Simon Potter, 2005. "Forecasting recessions using the yield curve," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(2), pages 77-103.
    6. J. Stevens, 2014. "Identification problems in Granger causality tests based on the net oil price increase," Applied Economics, Taylor & Francis Journals, vol. 46(1), pages 102-110, January.
    7. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-856, July.
    8. Dufour, Jean-Marie & Taamouti, Abderrahim, 2010. "Short and long run causality measures: Theory and inference," Journal of Econometrics, Elsevier, vol. 154(1), pages 42-58, January.
    9. Hansen, Bruce E, 1996. "Inference When a Nuisance Parameter Is Not Identified under the Null Hypothesis," Econometrica, Econometric Society, vol. 64(2), pages 413-430, March.
    10. Heikki Kauppi & Pentti Saikkonen, 2008. "Predicting U.S. Recessions with Dynamic Binary Response Models," The Review of Economics and Statistics, MIT Press, vol. 90(4), pages 777-791, November.
    11. Chen, Shiu-Sheng, 2009. "Predicting the bear stock market: Macroeconomic variables as leading indicators," Journal of Banking & Finance, Elsevier, vol. 33(2), pages 211-223, February.
    12. JAMES G. MacKINNON, 2006. "Bootstrap Methods in Econometrics," The Economic Record, The Economic Society of Australia, vol. 82(s1), pages 2-18, September.
    13. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
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    More about this item

    Keywords

    Spurious regressions; predictability; binary choice models.;

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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