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The Predictive Power of the Yield Spread under the Veil of Time

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  • Zagaglia, Paolo

    ()
    (Dept. of Economics, Stockholm University)

Abstract

I apply a multiresolution decomposition to the term spread and real-GDP growth in the U.S. Using the filtered data, I study whether the yield spread helps forecasting output. The results show that the predictive power of the yield spread varies largely across time scales both in-sample and out-of-sample at various forecast horizons. Contrarily to the existing literature, I find evidence of a strikingly negative long-run relationship between the spread and future GDP growth over a frequency that spans from 8 to 16 years per cycle. A linear combination among filtered yield spreads shows a sizable improvement in forecasting out-of-sample. The decomposed series are also used for proposing a solution to the breakdown in the in-sample predictive relationship documented by Dotsey (1998) that occurs after 1985.

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Bibliographic Info

Paper provided by Stockholm University, Department of Economics in its series Research Papers in Economics with number 2006:4.

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Length: 19 pages
Date of creation: 16 Jun 2006
Date of revision:
Handle: RePEc:hhs:sunrpe:2006_0004

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Postal: Department of Economics, Stockholm, S-106 91 Stockholm, Sweden
Phone: +46 8 16 20 00
Fax: +46 8 16 14 25
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Web page: http://www.ne.su.se/
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Keywords: wavelets; term structure; predictability;

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References

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Cited by:
  1. Kenneth S. Rogoff & Vania Stavrakeva, 2008. "The Continuing Puzzle of Short Horizon Exchange Rate Forecasting," NBER Working Papers 14071, National Bureau of Economic Research, Inc.

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