Macroeconomic Forecasting for the G7 countries under Uncertainty Shocks

2025-10-27

AI Summary

Traditional VAR-type models exhibit deteriorating forecast accuracy for G7 countries under geopolitical disruptions and policy uncertainty. Method: This paper proposes a Bayesian vector autoregression model with exogenous variables (BVARX), incorporating a novel four-dimensional uncertainty index—economic, financial, political, and geopolitical—constructed from news text. It innovatively integrates domain-knowledge-driven shrinkage priors, time–frequency domain wavelet coherence analysis, and nonlinear local projection methods to precisely capture state-dependent spillovers and time-varying dynamics. Contribution/Results: Relative to 14 benchmark models—including standard VAR, time-varying VAR (TV-VAR), and leading machine learning approaches—the proposed BVARX delivers statistically significant improvements in forecasting accuracy at both 12- and 24-month horizons. Comprehensive statistical validation confirms its robust uncertainty quantification capability and structural interpretability.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Game Theory and Economic Paradigms: Behavioral Game Theory

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Improving macroeconomic forecasting accuracy under uncertainty shocks
• Addressing overfitting and time-varying dependencies in VAR models
• Quantifying spillover effects of uncertainty shocks on core economic variables

Innovation

• Extended Bayesian VAR with exogenous uncertainty shocks
• Combined wavelet coherence with nonlinear local projections
• Outperformed benchmarks using robust uncertainty quantification