General Bayesian time‐varying parameter vector autoregressions for modeling government bond yields
开发了一种通用贝叶斯时变参数向量自回归模型，允许参数动态形式未知，并通过贝叶斯收缩先验进行模型选择，应用于美国收益率曲线建模与预测。
Summary US yield curve dynamics are subject to time‐variation, but there is ambiguity about its precise form. This paper develops a vector autoregressive (VAR) 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 form is supported by the data and to carry out model selection, we adopt Bayesian shrinkage priors. 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.