论文标题
推断贝叶斯添加剂载体自回旋树模型
Inference in Bayesian Additive Vector Autoregressive Tree Models
论文作者
论文摘要
向量自回旋（VAR）模型假设内源变量及其滞后之间存在线性。这个假设可能过于限制，可能会对预测准确性产生有害影响。作为解决方案，我们建议将VAR与贝叶斯添加剂回归树（BART）模型相结合。由此产生的贝叶斯添加剂载体自回旋树（Bavart）模型能够捕获内源变量与协变量之间的任意非线性关系，而研究人员没有太多输入。由于控制异质性是产生精确密度预测的关键，因此我们的模型允许在误差中进行随机波动。我们将模型应用于两个数据集。第一个申请表明，Bavart模型对美国利率的术语结构产生了高度竞争性的预测。在第二个应用程序中，我们使用适中的欧元区数据集估算了模型，以研究不确定性对经济的动态影响。
Vector autoregressive (VAR) models assume linearity between the endogenous variables and their lags. This assumption might be overly restrictive and could have a deleterious impact on forecasting accuracy. As a solution, we propose combining VAR with Bayesian additive regression tree (BART) models. The resulting Bayesian additive vector autoregressive tree (BAVART) model is capable of capturing arbitrary non-linear relations between the endogenous variables and the covariates without much input from the researcher. Since controlling for heteroscedasticity is key for producing precise density forecasts, our model allows for stochastic volatility in the errors. We apply our model to two datasets. The first application shows that the BAVART model yields highly competitive forecasts of the US term structure of interest rates. In a second application, we estimate our model using a moderately sized Eurozone dataset to investigate the dynamic effects of uncertainty on the economy.
