Long and short memory in dynamic term structure models

I provide a unified theoretical framework for long memory term structure models and show that the recent state-space approach suffers from a parameter identification problem. I propose a different framework to estimate long memory models in a state-space setup, which addresses the shortcomings of the existing approach. The proposed framework allows asymmetrically treating the physical and risk-neutral dynamics, which simplifies estimation considerably and helps to conduct an extensive comparison with standard term structure models. Relying on a battery of tests, I find that standard term structure models perform just as well as the more complicated long memory models and produce plausible term premium estimates.