Abstract
This paper studies the sources of cyclical information delivered
by the term spread for output growth predictability in the U.S. I use a
wavelet-based time-frequency decomposition to decompose the predictive power of
the yield spread across time scales, both in-sample and out-of-sample, over
various forecast horizons. Spreads between interest rates on 10-year and
3-month Treasuries have a predictive ability for output growth that changes
largely over different time scales. I find evidence of a negative correlation
between the spread and future GDP growth for fluctuations with a frequency of 4
to 8 years per cycle. A linear combination among filtered yield spreads shows a
sizable improvement in forecasting out-of-sample. The time-frequency
decomposition is also used to propose an interpretation for the breakdown of
in-sample predictability documented by Dotsey (1998) that arises after 1985.
JEL classification numbers: C19,
E43, E27.
Keywords: Multiresolution analysis, Term structure, Predictability.