Robust Econometrics for Growth-at-Risk

Tobias Adrian, Yuya Sasaki and Yulong Wang

Papers from arXiv.org

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.

Date: 2025-07
New Economics Papers: this item is included in nep-ecm and nep-fdg
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2508.00263

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