Robust inference under time‐varying volatility: A real‐time evaluation of professional forecasters
时变波动下的稳健推断：专业预测者的实时评估AI 解读 ↓
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时变波动下的稳健推断：专业预测者的实时评估
- 研究问题
- 研究时变波动如何破坏预测能力比较检验的有效性，以及如何在存在预测模型估计不确定性时进行稳健的实时评价。
- 方法与思路
- 作者提出 wild bootstrap 推断，覆盖既有检验及新的 CUSUM、Cramér–von Mises 检验，并允许滚动或递归估计。
- 材料陈述的贡献
- 摘要称该方法恢复渐近枢轴性；实时 SPF 应用显示，稳健临界值影响预测能力判断，专业预测优势也随时间变化。
- 阅读关注点
- 应核对波动过程和依赖结构条件、bootstrap 如何纳入估计误差，以及不同预测期限和比较基准下结论是否一致。
- 阅读边界
- 仅据所给 Crossref 摘要，不能推断其他版本变化；固定参数渐近方案的符号缺失，完整有效性条件与数值表现未说明。
原文摘要
Summary In many forecast evaluation applications, standard tests as well as tests allowing for time‐variation in relative forecast ability build on heteroskedasticity‐and‐autocorrelation consistent (HAC) covariance estimators. Yet, the finite‐sample performance of these asymptotics is often poor. “Fixed‐ ” asymptotics, used to account for long‐run variance estimation, improve finite‐sample performance under homoskedasticity, but lose asymptotic pivotality under time‐varying volatility. Moreover, loss of pivotality due to time‐varying volatility is found in the standard HAC framework in certain cases as well. We prove a wild bootstrap implementation to restore asymptotically pivotal inference for the above and new CUSUM‐ and Cramér‐von Mises‐based tests in a fairly general setup, allowing for estimation uncertainty from either a rolling window or a recursive approach when fixed‐ asymptotics are adopted to achieve good finite‐sample performance. We then investigate the (time‐varying) performance of professional forecasters relative to naive no‐change and model‐based predictions in real‐time. We exploit the Survey of Professional Forecasters (SPF) database and analyze nowcasts and forecasts at different horizons for output and inflation. We find that not accounting for time‐varying volatility seriously affects outcomes of tests for equal forecast ability: wild bootstrap inference typically yields convincing evidence for advantages of the SPF, while tests using non‐robust critical values provide remarkably less. Moreover, we find significant evidence for time‐variation of relative forecast ability, the advantages of the SPF weakening considerably after the “Great Moderation.”