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

Paolo Zagaglia

No 2006:4, Research Papers in Economics from Stockholm University, Department of Economics

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.

Keywords: wavelets; term structure; predictability (search for similar items in EconPapers)
JEL-codes: C19 E27 E43 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-for and nep-mac
Date: 2006-06-16
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