Pedro A. Lima, Carlos M. Carvalho, Hedibert F. Lopes, Andrew Herren
arXiv 17 Mar 2025 · Statistics — Methodology
arXiv:2503.13759 · PDF · Extracted main text
Vector autoregression (VAR) models are widely used for forecasting and macroeconomic analysis, yet they remain limited by their reliance on a linear parameterization. Recent research has introduced nonparametric alternatives, such as Bayesian additive regression trees (BART), which provide flexibility without strong parametric assumptions. However, existing BART-based frameworks do not account for time dependency or allow for sparse estimation in the construction of regression tree priors, leading to noisy and inefficient high-dimensional representations. This paper introduces a sparsity-inducing Dirichlet hyperprior on the regression tree's splitting probabilities, allowing for automatic variable selection and high-dimensional VARs. Additionally, we propose a structured shrinkage prior that decreases the probability of splitting on higher-order lags, aligning with the Minnesota prior's principles. Empirical results demonstrate that our approach improves predictive accuracy over the baseline BART prior and Bayesian VAR (BVAR), particularly in capturing time-dependent relationships and enhancing density forecasts. These findings highlight the potential of developing domain-specific nonparametric methods in macroeconomic forecasting.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Chipman, H. A., George, E. I., and McCulloch, R. E (2010) Bart: Bayesian additive regression trees | 1.000 | 6 | 4 | 100% |
| 2 | Huber, F. and Rossini, L (2022) Inference in bayesian additive vector autoregressive tree models | 1.000 | 5 | 4 | 100% |
| 3 | Linero, A. R (2018) Bayesian regression trees for high-dimensional prediction and variable selection | 0.928 | 4 | 3 | 100% |
| 4 | Aguilar, O. and West, M (2000) Bayesian dynamic factor models and portfolio allocation | 0.737 | 3 | 2 | 100% |
| 5 | Chan, J. C (2020) Large Bayesian vector autoregressions | 0.644 | 2 | 2 | 100% |
| 6 | Doan, T., Litterman, R., and Sims, C (1984) Forecasting and conditional projection using realistic prior distributions | 0.644 | 2 | 2 | 100% |
| 7 | Kastner, G. and Huber, F (2020) Sparse bayesian vector autoregressions in huge dimensions | 0.644 | 2 | 2 | 100% |
| 8 | Koop, G. M (2013) Forecasting with medium and large bayesian vars | 0.644 | 2 | 2 | 100% |
| 9 | McCracken, M. W. and Ng, S (2016) Fred-md: A monthly database for macroeconomic research | 0.511 | 2 | 2 | 50% |
| 10 | Bańbura, M., Giannone, D., and Reichlin, L (2010) Large bayesian vector auto regressions | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 38 scored citations.