General Bayesian time-varying parameter VARs for modeling government bond yields

US yield curve dynamics are subject to time-variation, but there is ambiguity on its precise form. This paper develops a vector autoregressive model with time-varying parameters and stochastic volatility which treats the nature of parameter dynamics as unknown. Coefficients can evolve according to a random walk, a Markov switching process, observed predictors or depend on a mixture of these. To decide which is supported by the data, we adopt Bayesian shrinkage priors to carry out model selection. Our framework is applied to model the US yield curve. We show that the model forecasts well, and focus on selected in-sample features to analyze determinants of structural breaks in US yield curve dynamics.