Shovon Sengupta, Sunny Kumar Singh, Tanujit Chakraborty
arXiv 27 Oct 2025 · Econometrics
arXiv:2510.23347 · PDF · DOI · OpenAlex · Extracted main text
Accurate macroeconomic forecasting has become harder amid geopolitical disruptions, policy reversals, and volatile financial markets. Conventional vector autoregressions (VARs) overfit in high dimensional settings, while threshold VARs struggle with time varying interdependencies and complex parameter structures. We address these limitations by extending the Sims Zha Bayesian VAR with exogenous variables (SZBVARx) to incorporate domain-informed shrinkage and four newspaper based uncertainty shocks such as economic policy uncertainty, geopolitical risk, US equity market volatility, and US monetary policy uncertainty. The framework improves structural interpretability, mitigates dimensionality, and imposes empirically guided regularization. Using G7 data, we study spillovers from uncertainty shocks to five core variables (unemployment, real broad effective exchange rates, short term rates, oil prices, and CPI inflation), combining wavelet coherence (time frequency dynamics) with nonlinear local projections (state dependent impulse responses). Out-of-sample results at 12 and 24 month horizons show that SZBVARx outperforms 14 benchmarks, including classical VARs and leading machine learning models, as confirmed by Murphy difference diagrams, multivariate Diebold Mariano tests, and Giacomini White predictability tests. Credible Bayesian prediction intervals deliver robust uncertainty quantification for scenario analysis and risk management. The proposed SZBVARx offers G7 policymakers a transparent, well calibrated tool for modern macroeconomic forecasting under pervasive uncertainty.
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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 | Caldara, D., & Iacoviello, M (2022) Measuring geopolitical risk | 1.000 | 10 | 3 | 100% |
| 2 | Sims, C. A., & Zha, T (1998) Bayesian methods for dynamic multivariate models | 1.000 | 8 | 3 | 100% |
| 3 | Bańbura, M., Giannone, D., & Reichlin, L (2010) Large bayesian vector auto regressions | 1.000 | 6 | 4 | 100% |
| 4 | Giannone, D., Lenza, M., & Primiceri, G. E (2015) Prior selection for vector autoregressions | 0.928 | 4 | 3 | 100% |
| 5 | Baker, S. R., Bloom, N., & Davis, S. J (2016) Measuring economic policy uncertainty | 0.874 | 9 | 2 | 100% |
| 6 | Bloom, N (2009) The impact of uncertainty shocks | 0.874 | 5 | 2 | 100% |
| 7 | Jordà, Ò (2005) Estimation and inference of impulse responses by local projections | 0.843 | 4 | 3 | 75% |
| 8 | Husted, L., Rogers, J., & Sun, B (2020) Monetary policy uncertainty | 0.811 | 4 | 2 | 100% |
| 9 | Lhuissier, S., & Tripier, F (2021) Regime-dependent effects of uncertainty shocks: A structural interpretation | 0.811 | 4 | 2 | 100% |
| 10 | Caldara, D., Fuentes-Albero, C., Gilchrist, S., & Zakrajsek, E (2016) The macroeconomic impact of financial and uncertainty shocks | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 156 scored citations.