Credit Spreads as Predictors of Real-Time Economic Activity: A Bayesian Model-Averaging Approach
使用贝叶斯模型平均方法，结合大量金融指标（尤其是信用利差），预测当前至未来四个季度的经济活动，发现信用利差能显著提升预测准确性。
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.