arXiv 13 Jun 2025 · Econometrics
arXiv:2506.11551 · PDF · DOI · OpenAlex · Extracted main text
This article proposes a novel framework that integrates Bayesian Additive Regression Trees (BART) into a Factor-Augmented Vector Autoregressive (FAVAR) model to forecast macro-financial variables and examine asymmetries in the transmission of oil price shocks. By employing nonparametric techniques for dimension reduction, the model captures complex, nonlinear relationships between observables and latent factors that are often missed by linear approaches. A simulation experiment comparing FABART to linear alternatives and a Monte Carlo experiment demonstrate that the framework accurately recovers the relationship between latent factors and observables in the presence of nonlinearities, while remaining consistent under linear data-generating processes. The empirical application shows that FABART substantially improves forecast accuracy for industrial production relative to linear benchmarks, particularly during periods of heightened volatility and economic stress. In addition, the model reveals pronounced sign asymmetries in the transmission of oil supply news shocks to the U.S. economy, with positive shocks generating stronger and more persistent contractions in real activity and inflation than the expansions triggered by negative shocks. A similar pattern emerges at the U.S. federal state level, where negative shocks lead to modest declines in employment compared to the substantially larger contractions observed after positive shocks.
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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 | Huber, F., Koop, G., Onorante, L., Pfarrhofer, M., and Schreiner, J (2020) Nowcasting in a pandemic using non-parametric mixed frequency VARs | 1.000 | 5 | 3 | 100% |
| 2 | Hauzenberger, N., Huber, F., and Klieber, K (2023) Real-time inflation forecasting using non-linear dimension reduction techniques | 0.928 | 4 | 3 | 100% |
| 3 | Känzig, D. R (2021) The macroeconomic effects of oil supply news: Evidence from opec announcements | 0.849 | 12 | 2 | 92% |
| 4 | Hamilton, J. D (2003) What is an oil shock? | 0.811 | 4 | 2 | 100% |
| 5 | Baumeister, C., Huber, F., and Marcellino, M (2024) Risky oil: It's all in the tails | 0.763 | 9 | 2 | 67% |
| 6 | Bańbura, M., Giannone, D., and Reichlin, L (2010) Large bayesian vector auto regressions | 0.737 | 4 | 3 | 50% |
| 7 | Alessandri, P. and Mumtaz, H (2017) Financial conditions and density forecasts for US output and inflation | 0.737 | 4 | 2 | 75% |
| 8 | Balke, N. S., Brown, S. P., and Yucel, M. K (2002) Oil price shocks and the us economy: Where does the asymmetry originate? | 0.737 | 3 | 2 | 100% |
| 9 | Chipman, H. A., George, E. I., and McCulloch, R. E (2010) BART: Bayesian additive regression trees | 0.693 | 7 | 1 | 100% |
| 10 | Mumtaz, H., Sunder-Plassmann, L., and Theophilopoulou, A (2018) The state-level impact of uncertainty shocks | 0.693 | 6 | 2 | 50% |
Showing the top 10 of 53 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | A Nonparametric Approach to Augmenting a Bayesian VAR with Nonlinear Factors | 0.585 | 3 | 1 |