Abstract
In this paper we examine the out-of-sample forecast performance of high-yield credit spreads for real-time and revised data regarding employment and industrial production in the US. We evaluate models using both a point forecast and a probability forecast exercise. Our main findings suggest that the best results come from using only a few factors obtained by pooling information from a number of sector-specific high-yield credit spreads. In particular, for employment and at short-run horizons, there is a gain from using a principal components model fitted to high-yield credit spreads compared to the prediction produced by benchmarks. Moreover, forecast results based on revised data are qualitatively similar to those obtained using real-time data
Lingua originale | English |
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Numero di pagine | 12 |
Rivista | Journal of Macroeconomics |
Volume | 32 |
Stato di pubblicazione | Published - 2010 |
All Science Journal Classification (ASJC) codes
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