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.
翻译:时间变换参数( TVP) 回归通常假定系数的时间变换是由简单的随机行走等随机随机随机的随机随机过程决定的。 虽然这些模型能够捕捉到一系列广泛的动态模式, 但时间变异的真实性质可能来自其他来源, 或来自不同的运动法则。 在本文中, 我们提议一个灵活的 TVP VAR, 假设TVP 依赖于部分潜伏的共变数的面板。 这些共变数的潜在部分在状态动态上有所不同, 从而捕捉到平稳变化或突变的系数。 为了确定这些共变数中哪些是重要因素, 从而决定适当的国家演变, 我们引入了贝叶省缩缩缩前期来进行模型选择。 作为实验性应用, 我们预测美国利率的术语结构, 并显示我们的方法与一组竞争模型相比表现良好。 然后我们展示如何使用模型来解释与美国收益曲线相关的系数的结构性折损。