Robust Econometrics for Growth-at-Risk
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908884461944832 |
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| author | Adrian, Tobias Sasaki, Yuya Wang, Yulong |
| author_facet | Adrian, Tobias Sasaki, Yuya Wang, Yulong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_00263 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robust Econometrics for Growth-at-Risk Adrian, Tobias Sasaki, Yuya Wang, Yulong Econometrics 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. |
| title | Robust Econometrics for Growth-at-Risk |
| topic | Econometrics |
| url | https://arxiv.org/abs/2508.00263 |