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Evaluating a Leading Indicator: An Application: the Term Spread

Listed author(s):
  • Herman O. Stekler

    ()

    (The George Washington University)

  • Tianyu Ye

    (The George Washington University)

This paper analyzes the procedures that have previously been used to evaluate indicators. These methods determine whether the indicator correctly classifies periods when there was (not) a recession. These approaches do not show whether or not an indicator signaled a turn or failed to predict it. This paper then presents a new approach and applies it to the term spread series. The results are mixed because the indicator predicts every recession but also generates a large number of false signals. This result may explain why economists do not always place great weight on this series.

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File URL: https://www2.gwu.edu/~forcpgm/2016-004.pdf
File Function: First version, 2016
Download Restriction: no

Paper provided by The George Washington University, Department of Economics, Research Program on Forecasting in its series Working Papers with number 2016-004.

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Length: 23 pages
Date of creation: Mar 2016
Handle: RePEc:gwc:wpaper:2016-004
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Web page: https://www2.gwu.edu/~forcpgm
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  1. S. S. Alexander & H. O. Stekler, 1959. "Forecasting Industrial Production--Leading Series versus Autoregression," Journal of Political Economy, University of Chicago Press, vol. 67, pages 402-402.
  2. Lahiri, Kajal & Yang, Liu, 2015. "A further analysis of the conference board’s new Leading Economic Index," International Journal of Forecasting, Elsevier, vol. 31(2), pages 446-453.
  3. Estrella, Arturo & Hardouvelis, Gikas A, 1991. " The Term Structure as a Predictor of Real Economic Activity," Journal of Finance, American Finance Association, vol. 46(2), pages 555-576, June.
  4. Arturo Estrella & Anthony P. Rodrigues & Sebastian Schich, 2003. "How Stable is the Predictive Power of the Yield Curve? Evidence from Germany and the United States," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 629-644, August.
  5. Raffaella Giacomini & Barbara Rossi, 2006. "How Stable is the Forecasting Performance of the Yield Curve for Output Growth?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 68(s1), pages 783-795, December.
  6. Bryan Boulier & H. O. Stekler, 2001. "The term spread as a cyclical indicator: a forecasting evaluation," Applied Financial Economics, Taylor & Francis Journals, vol. 11(4), pages 403-409.
  7. Lahiri, Kajal & Monokroussos, George & Zhao, Yongchen, 2013. "The yield spread puzzle and the information content of SPF forecasts," Economics Letters, Elsevier, vol. 118(1), pages 219-221.
  8. Marcelle Chauvet & Simon Potter, 2005. "Forecasting recessions using the yield curve," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(2), pages 77-103.
  9. Kajal Lahiri & J George Wang, 2006. "Subjective Probability Forecasts for Recessions," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 41(2), pages 26-37, April.
  10. Benjamin M. Friedman & Kenneth N. Kuttner, 1998. "Indicator Properties Of The Paper-Bill Spread: Lessons From Recent Experience," The Review of Economics and Statistics, MIT Press, vol. 80(1), pages 34-44, February.
  11. Travis J. Berge, 2015. "Predicting Recessions with Leading Indicators: Model Averaging and Selection over the Business Cycle," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(6), pages 455-471, 09.
  12. Lahiri, Kajal & Wang, J. George, 2013. "Evaluating probability forecasts for GDP declines using alternative methodologies," International Journal of Forecasting, Elsevier, vol. 29(1), pages 175-190.
  13. Rudebusch, Glenn D. & Williams, John C., 2009. "Forecasting Recessions: The Puzzle of the Enduring Power of the Yield Curve," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 492-503.
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