The Predictive Power of Nelson-Siegel Factor Loadings for the Real Economy

We generalize the arbitrage-free Nelson Siegel (AFNS) model to allow λt to vary over time. We find that the time-varying λt, which determines the relative factor loadings, typically reaches its local peak before starting to decline right before a recession. Through conducting extensive in-sample and out-of-sample forecast exercises, we show that the information in the time-varying λt factor has strong predictive power for business cycles and real economic activity. In particular, λt contains additional useful information beyond those in conventional yield curve predictors, such as the yield spread. We argue and also document empirical evidence that the information in λt is related to the market perception of the economic risk and uncertainty