Using micro-level data, we construct a credit spread index with considerable predictive power for future economic activity. We decompose the credit spread into a component that captures firm-specific information on expected defaults and a residual component–– the excess bond premium. Shocks to the excess bond premium that are orthogonal to the current state of the economy lead to declines in economic activity and asset prices. An increase in the excess bond premium appears to reflect a reduction in the risk-bearing capacity of the financial sector, which induces a contraction in the supply of credit and a deterioration in macroeconomic conditions.

This paper develops a general equilibrium model with putty‐clay technology, investment irreversibility, and variable capacity utilization. Low short‐run capital‐labor substitutability induces the putty‐clay effect of a tight link between changes in capacity and movements in employment and output. Permanent shocks to technology or factor prices generate a hump‐shaped response of hours, persistence in output growth, and positive comovement in the forecastable components of output and hours. Capacity constraints result in asymmetric responses to large shocks with recessions deeper than expansions. Estimation of a two‐sector model supports a significant role for putty‐clay capital in explaining business cycle and medium‐run dynamics.

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.