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Robust Econometrics for Growth-at-Risk

Author

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  • Tobias Adrian
  • Yuya Sasaki
  • Yulong Wang

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.

Suggested Citation

  • Tobias Adrian & Yuya Sasaki & Yulong Wang, 2025. "Robust Econometrics for Growth-at-Risk," Papers 2508.00263, arXiv.org.
    • Handle: RePEc:arx:papers:2508.00263
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    References listed on IDEAS

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    1. Li, Qi & Racine, Jeffrey S, 2008. "Nonparametric Estimation of Conditional CDF and Quantile Functions With Mixed Categorical and Continuous Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 423-434.
    2. Gächter, Martin & Hasler, Elias & Huber, Florian, 2025. "A tale of two tails: 130 years of growth at risk," Macroeconomic Dynamics, Cambridge University Press, vol. 29, pages 1-1, January.
    3. Huixia Judy Wang & Deyuan Li, 2013. "Estimation of Extreme Conditional Quantiles Through Power Transformation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(503), pages 1062-1074, September.
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