Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
This paper employs a factor-augmented dynamic Nelson–Siegel (FADNS) model to predict the yield curve in the US that relies on a large data set of mostly forward-looking macroeconomic variables. FADNS models significantly improve interest rate forecasts relative to many extant models in the literature. For longer horizons, it outperforms autoregressive alternatives, with reductions in mean absolute forecast error of up to 18% using quarterly data and of up to 40% at higher frequencies. For shorter horizons, it is still competitive against autoregressive forecasts, outclassing them for 7- and 10-year yields. The out-of-sample analysis reveals that the forward-looking nature of the indicators we employ is crucial for improving forecasting performance. Including them indeed reduces the mean absolute error with respect to specifications based on backward-looking macroeconomic indicators for any model we consider.