| Content | We propose an Adaptive Dynamic Nelson-Siegel (ADNS) model to adaptively forecast the yield curve. The model has a simple yet flexible structure and can be safely applied to both stationary and nonstationary situations with different sources of change. For the 3- to 12-months ahead out-of-sample forecasts of the US yield curve from 1998:1 to 2010:9, the ADNS model dominates both the dynamic Nelson-Siegel (DNS) and random walk models, reducing the forecast error measurements by between 30 and 60 percent. The locally estimated coefficients and the identified stable subsamples over time align with policy changes and the timing of the recent financial crisis. |
