Credit Spreads as Predictors of Real-Time Economic Activity: A Bayesian Model-Averaging Approach
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.)
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Volume (Year): 95 (2013)
Issue (Month): 5 (December)
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- Cameron, A. Colin & Gelbach, Jonah B. & Miller, Douglas L., 2011.
"Robust Inference With Multiway Clustering,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 29(2), pages 238-249.
- A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller & Doug Miller, 2009. "Robust Inference with Multi-way Clustering," Working Papers 98, University of California, Davis, Department of Economics.
- Jonah B. Gelbach & Doug Miller, 2009. "Robust Inference with Multi-way Clustering," Working Papers 99, University of California, Davis, Department of Economics.
- A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2006. "Robust Inference with Multi-way Clustering," NBER Technical Working Papers 0327, National Bureau of Economic Research, Inc.
- Sreedhar T. Bharath & Tyler Shumway, 2008. "Forecasting Default with the Merton Distance to Default Model," Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1339-1369, May.
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