Forecasting Interest Rates with Shifting Endpoints

Existing studies on interest rate forecasting either treat yields as being stationary around a fixed mean or as a random walk process. In this study we consider forecasting the term structure of interest rates with the assumption that the yield curve is driven by factors that are stationary around a slowly time-varying mean or "shifting endpoint". The shifting endpoints are captured using either (i) time series methods (exponential smoothing), or (ii) long-range survey forecasts of either interest rates or inflation and output growth, or (iii) exponentially smoothed realizations of these macro variables. We find that allowing for shifting endpoints in yield curve factors can provide gains in the out-of-sample predictive accuracy, relative to stationary and random walk benchmarks. These gains are statistically significant, and can involve more than 20 percent reductions in root mean square prediction error