Credit Spreads as Predictors of Real-Time Economic Activity: A Bayesian Model-Averaging Approach

Jon Faust, Simon Gilchrist, Jonathan Wright and Egon Zakrajšsek
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Jon Faust: Johns Hopkins University, Federal Reserve Board, and NBER
Egon Zakrajšsek: Federal Reserve Board

The Review of Economics and Statistics, 2013, vol. 95, issue 5, 1501-1519

Abstract: Employing a large number of financial indicators, we use Bayesian model averaging (BMA) to forecast real-time measures of economic activity. The indicators include credit spreads based on portfolios, constructed directly from the secondary market prices of outstanding bonds, sorted by maturity and credit risk. Relative to an autoregressive benchmark, BMA yields consistent improvements in the prediction of the cyclically sensitive measures of economic activity at horizons from the current quarter out to four quarters hence. The gains in forecast accuracy are statistically significant and economically important and owe almost exclusively to the inclusion of credit spreads in the set of predictors. (No rights reserved. This work was authored as part of the Contributor's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. law.)

Keywords: forecasting; real-time data; Bayesian Model Averaging; credit spreads (search for similar items in EconPapers)
JEL-codes: C11 C53 (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (118)

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Related works:
Working Paper: Credit spreads as predictors of real-time economic activity: a Bayesian Model-Averaging approach (2012)
Working Paper: Credit Spreads as Predictors of Real-Time Economic Activity: A Bayesian Model-Averaging Approach (2011)
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