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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Paper provided by Stockholm University, Department of Economics in its series Research Papers in Economics with number
2006:4.
Length: 19 pages Date of creation: 16 Jun 2006 Date of revision: Handle: RePEc:hhs:sunrpe:2006_0004
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Find related papers by JEL classification: C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Other E27 - Macroeconomics and Monetary Economics - - Macroeconomics: Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Determination of Interest Rates; Term Structure of Interest Rates
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