Robust econometrics for growth-at-risk
针对增长风险框架中隐含的恒定帕累托指数假设，提出新的稳健计量方法估计尾部，模拟和长期分析显示预测精度优于现有方法，能更好捕捉金融异常。
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.