A Flexible Approach to Augmenting a Bayesian VAR with Nonlinear Factors

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.