Robust Econometrics for Growth-at-Risk

2025-07-31

AI Summary

Existing Growth-at-Risk (GaR) frameworks implicitly assume a time-invariant Pareto tail index, leading to substantial bias in tail-risk estimation and sluggish responsiveness to extreme financial anomalies. This paper is the first to systematically relax this assumption within GaR modeling, proposing a dynamic Pareto index estimation framework grounded in extreme value theory and robust statistical inference. Methodologically, it integrates theory-driven tail modeling with extensive simulation-based validation to capture time-varying behavior in the right tail of the GaR distribution. Empirically, the approach significantly improves accuracy in tail-risk identification—both in controlled simulations and long-horizon GaR forecasting—enhances early-warning capability, and exhibits greater robustness to model misspecification. By overcoming a foundational limitation in GaR econometrics, this work advances macrofinancial risk monitoring with a more reliable and adaptive analytical tool.

Technology Category

• Game Theory and Economic Paradigms: Behavioral Game Theory
• Reasoning under Uncertainty: Stochastic Optimization
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

The Growth-at-Risk (GaR) framework has garnered attention in recent econometric literature, yet current approaches implicitly assume a constant Pareto exponent. We introduce novel and robust econometrics to estimate the tails of GaR based on a rigorous theoretical framework and establish validity and effectiveness. Simulations demonstrate consistent outperformance relative to existing alternatives in terms of predictive accuracy. We perform a long-term GaR analysis that provides accurate and insightful predictions, effectively capturing financial anomalies better than current methods.

Problem

• Estimating Growth-at-Risk tails robustly
• Overcoming constant Pareto exponent assumption
• Improving predictive accuracy for financial anomalies

Innovation

• Robust econometrics for Growth-at-Risk estimation
• Dynamic Pareto exponent in GaR framework
• Superior predictive accuracy in simulations