A Flexible Approach to Augmenting a Bayesian VAR with Nonlinear Factors (opens in new tab)

arXiv:2508.13972v2 Announce Type: replace-cross Abstract: 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, mod...