We forecast the term structure of U.S. Treasury zero-coupon bond yields by analyzing a range of models that have been used in the literature. We assess the relevance of parameter uncertainty by examining the added value of using Bayesian inference compared to frequentist estimation techniques, and model uncertainty by combining forecasts from individual models. Following current literature we also investigate the benefits of incorporating macroeconomic information in yield curve models. Our results show that adding macroeconomic factors is very beneficial for improving the out-of-sample forecasting performance of individual models. Despite this, the predictive accuracy of models varies over time considerably, irrespective of using the Bayesian or frequentist approach. We show that mitigating model uncertainty by combining forecasts leads to substantial gains in forecasting performance, especially when applying Bayesian model averaging.

Additional Metadata
Keywords Affine term structure model, Bayesian analysis, Nelson-Spiegel model, forecast combination, term structure of interest rates
JEL C11, Bayesian Analysis (jel), C32, Time-Series Models; Dynamic Quantile Regressions (jel), C5, Econometric Modeling (jel), E43, Determination of Interest Rates; Term Structure of Interest Rates (jel), E47, Forecasting and Simulation (jel), F47, Forecasting and Simulation (jel)
Persistent URL hdl.handle.net/1765/9148
de Pooter, M.D, Ravazzolo, F, & van Dijk, D.J.C. (2007). Predicting the Term Structure of Interest Rates: Incorporating parameter uncertainty, model uncertainty and macroeconomic information (No. TI 07-028/4). Discussion paper / Tinbergen Institute. Retrieved from http://hdl.handle.net/1765/9148