# Inference in Bayesian Additive Vector Autoregressive Tree Models

arXiv: https://arxiv.org/abs/2006.16333 · HTML: https://mlsanity.com/paper/2006.16333/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2020.

## Built on by (most cited first, 5 of 5)

- [Macroeconomic Forecasting with Large Language Models](https://mlsanity.com/paper/2407.00890/) (2024)
- [From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks](https://mlsanity.com/paper/2311.16333/) (2023)
- [Bayesian Nonlinear Regression using Sums of Simple Functions](https://mlsanity.com/paper/2312.01881/) (2023)
- [Predictive Density Combination Using a Tree-Based Synthesis Function](https://mlsanity.com/paper/2311.12671/) (2023)
- [Generative Regression with IQ-BART](https://mlsanity.com/paper/2507.04168/) (2025)

## Abstract (arXiv)

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.