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

Jon Faust; Simon Gilchrist; Jonathan H. Wright; Egon Zakrajšsek

doi:10.1162/rest_a_00376

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

Jon Faust¹; Simon Gilchrist²; Jonathan H. Wright³; Egon Zakrajšsek⁴

¹ Johns Hopkins University, Federal Reserve Board, and NBER · ² Boston University and NBER · ³ Johns Hopkins University and NBER · ⁴ Federal Reserve Board

The Review of Economics and Statistics 2013

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.

DOI: 10.1162/rest_a_00376
Volume: 95 (5)
Pages: 1501-1519
Language: en
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