Paolo Zagaglia: Dept. of Economics, Stockholm University, Postal: Department of Economics, Stockholm University, S-106 91 Stockholm, Sweden
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.
19 pages, June 16, 2006
Full text files
Questions (including download problems) about the papers in this series should be directed to Sten Nyberg ()
Report other problems with accessing this service to Sune Karlsson ().
This page generated on 2018-01-23 23:38:23.