This paper proposes a vector autoregression augmented with nonlinear factors that are modeled nonparametrically using regression trees. There are four main advantages of our model. First, the use of factor methods ensures that departures from linearity are modeled parsimoniously. In particular, they exhibit functional pooling where a small number of nonlinear factors are used to model common nonlinearities across variables. Second, modeling potential nonlinearities nonparametrically lessens the risk of misspecification. Third, Bayesian computation using MCMC is straightforward even in very high-dimensional models, allowing for efficient, equation-by-equation estimation, thus avoiding computational bottlenecks that arise in popular alternatives such as the time-varying parameter VAR. Fourth, existing methods for identifying structural economic shocks in linear factor models can be adapted for the nonlinear case in a straightforward fashion using our model. Exercises involving artificial and macroeconomic data illustrate the properties of our model and its usefulness for forecasting and structural economic analysis.

翻译：本文提出一种向量自回归模型，该模型通过回归树以非参数方式建模非线性因子，并对其进行增强。我们的模型具有四大优势。第一，因子方法的使用确保模型以简约方式刻画非线性偏离，特别是其展现出函数池化特性，即通过少量非线性因子对变量间的共同非线性特征进行建模。第二，以非参数方式对潜在非线性进行建模降低了模型误设风险。第三，即使在极高维模型中，基于MCMC的贝叶斯计算方法仍可高效实现逐方程估计，因而避免了时变参数VAR等主流替代方法中存在的计算瓶颈。第四，现有线性因子模型中的结构性经济冲击识别方法，可通过我们的模型以直接方式适用于非线性情形。基于人工数据和宏观经济数据的实证分析，展示了本模型的特性及其在预测与结构性经济分析中的实用价值。