  Time-varying parameter (TVP) regressions commonly assume that time-variation
in the coefficients is determined by a simple stochastic process such as a
random walk. While such models are capable of capturing a wide range of dynamic
patterns, the true nature of time variation might stem from other sources, or
arise from different laws of motion. In this paper, we propose a flexible TVP
VAR that assumes the TVPs to depend on a panel of partially latent covariates.
The latent part of these covariates differ in their state dynamics and thus
capture smoothly evolving or abruptly changing coefficients. To determine which
of these covariates are important, and thus to decide on the appropriate state
evolution, we introduce Bayesian shrinkage priors to perform model selection.
As an empirical application, we forecast the US term structure of interest
rates and show that our approach performs well relative to a set of competing
models. We then show how the model can be used to explain structural breaks in
coefficients related to the US yield curve.
