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arXiv · 2508.00263

Robust Econometrics for Growth-at-Risk

Tobias Adrian·Yuya Sasaki·Yulong Wang
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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.

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Tobias Adrian, Yuya Sasaki, Yulong Wang. 2025-08-01. Robust Econometrics for Growth-at-Risk. https://arxiv.org/abs/2508.00263

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