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

  • Jon Faust

    (Johns Hopkins University, Federal Reserve Board, and NBER)

  • Simon Gilchrist

    (Boston University and NBER)

  • Jonathan H. Wright

    (Johns Hopkins University and NBER)

  • Egon Zakrajšsek

    (Federal Reserve Board)

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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File URL: http://www.mitpressjournals.org/doi/pdf/10.1162/REST_a_00376
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Article provided by MIT Press in its journal Review of Economics and Statistics.

Volume (Year): 95 (2013)
Issue (Month): 5 (December)
Pages: 1501-1519

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Handle: RePEc:tpr:restat:v:95:y:2013:i:5:p:1501-1519
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  1. 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.
  2. 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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