Large Bayesian Additive Vector Autoregressive Tree Models

Music City Center 

Vector autoregression (VAR) models are crucial for forecasting and analyzing macroeconomic variables, serving as a fundamental tool for applied macroeconomists. Recent literature has explored nonparametric approaches, such as Bayesian additive regression trees (BART), which allow for flexibility without strong parametric assumptions; however, existing frameworks like that proposed by (Huber and Rossini, 2022) do not adequately accommodate high-dimensional data or time dependency in the prior construction. This study enhances the literature by extending previous work to enable high-dimensional data analysis and variable selection through a sparsity-inducing Dirichlet hyperprior, as in Linero (2018) on the regression tree's splitting probabilities, while also proposing a prior that decrease the probability of splitting on variables that are have higher lags than smaller lags, similar to the approach taken by the Minnesota Prior. Empirical results show improvement compared to the baseline BART prior structure and a BVAR.

High-dimentional data

Regression trees

Sparcity-inducing priors

Multivariate Time Series