Credit spreads as predictors of real-time economic activity: a Bayesian Model-Averaging approach
AbstractEmploying 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.
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Bibliographic InfoPaper provided by Board of Governors of the Federal Reserve System (U.S.) in its series Finance and Economics Discussion Series with number 2012-77.
Date of creation: 2012
Date of revision:
Other versions of this item:
- Jon Faust & Simon Gilchrist & Jonathan H. Wright & Egon Zakrajsek, 2011. "Credit Spreads as Predictors of Real-Time Economic Activity: A Bayesian Model-Averaging Approach," NBER Working Papers 16725, National Bureau of Economic Research, Inc.
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
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- Liebermann, Joelle, 2012.
"Real-time forecasting in a data-rich environment,"
39452, University Library of Munich, Germany.
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