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




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.

Suggested Citation

  • Zagaglia, Paolo, 2006. "The Predictive Power of the Yield Spread under the Veil of Time," Research Papers in Economics 2006:4, Stockholm University, Department of Economics.
  • Handle: RePEc:hhs:sunrpe:2006_0004

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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.

    More about this item


    wavelets; term structure; predictability;

    JEL classification:

    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects

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