论文标题
使用回归树对TVP-VAR的贝叶斯建模
Bayesian Modeling of TVP-VARs Using Regression Trees
论文作者
论文摘要
鉴于宏观经济模型中参数不稳定性的广泛证据，已经提出了许多时间变化的参数（TVP）模型。本文提出了使用贝叶斯添加剂回归树（BART）的非参数TVP-VAR模型，该模型将TVPS建模为效应修饰符的未知函数。该模型的新颖性源于以下事实：驱动参数的运动定律是非参数处理的。这在条件平均值和条件差异中都在参数变化的性质和程度上具有很大的灵活性。通过采用非参数因子结构和使用收缩先验的使用来实现简约。在应用于宏观经济数据的应用中，我们说明了我们的模型在跟踪菲利普斯曲线的不断发展的性质以及商业周期冲击对通货膨胀量的影响如何随着效应修饰符的变化而变化而变化。
In light of widespread evidence of parameter instability in macroeconomic models, many time-varying parameter (TVP) models have been proposed. This paper proposes a nonparametric TVP-VAR model using Bayesian additive regression trees (BART) that models the TVPs as an unknown function of effect modifiers. The novelty of this model arises from the fact that the law of motion driving the parameters is treated nonparametrically. This leads to great flexibility in the nature and extent of parameter change, both in the conditional mean and in the conditional variance. Parsimony is achieved through adopting nonparametric factor structures and use of shrinkage priors. In an application to US macroeconomic data, we illustrate the use of our model in tracking both the evolving nature of the Phillips curve and how the effects of business cycle shocks on inflation measures vary nonlinearly with changes in the effect modifiers.
