| Content | We propose an Adaptive Dynamic Nelson–Siegel (ADNS) model to adaptively detect parameter changes
and forecast the yield curve. The model is simple yet flexible and can be safely applied to both stationary
and nonstationary situations with different sources of parameter changes. 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 popular reduced-form and affine term structure models; compared to random walk prediction, the
ADNS steadily reduces the forecast error measurements by between 20% and 60%. The locally estimated
coefficients and the identified stable subsamples over time align with policy changes and the timing of
the recent financial crisis. |
