AI 中文总结
研究宏观经济预测中修正风险在发布周期的演变，通过分解后期结果MSE，表明其不确定性部分可识别，产生弗雷歇 - 马卡罗夫集，推动多种传输方法，样本外结果支持依覆盖率和稳定性选择方法。
AI 中文摘要
宏观经济预测是先发布然后再修正的结果。首次国内生产总值（GDP）发布的90%区间、六个月数值或最新值基准并非相同的不确定性表述。我们探讨修正风险在发布周期中如何演变，以及在后期结果误差稀少时实时能报告什么。我们将后期结果均方误差（MSE）分解为初步预测风险、修正风险及其协方差。在专业预测者调查（SPF）数据中，首次发布到约180天修正占实际活动目标后期结果MSE的8.3%，而通胀目标为3.6%。我们表明后期结果不确定性部分可识别：已发布历史给出早期误差和修正边际，但不是它们的依赖关系。这产生了一个尖锐的弗雷歇 - 马卡罗夫集，并推动直接后期校准、依赖稳健传输以及有符号或修正模型传输。样本外结果支持方法选择而非通用传输规则：覆盖率和稳定性决定何时传输收益可用。
英文摘要
Macroeconomic forecasts refer to outcomes that are first released and then revised. A 90 percent interval for the first GDP release, a six-month value, or a latest-value benchmark is not the same uncertainty statement. We ask how revision risk evolves through the release cycle and what can be reported in real time when later-outcome errors are scarce. We decompose later-outcome MSE into preliminary forecast risk, revision risk, and their covariance. In SPF data, first-release to roughly 180-day revisions account for 8.3 percent of later-outcome MSE across real-activity targets, versus 3.6 percent across inflation targets. We show that later-outcome uncertainty is partially identified: released histories give early-error and revision marginals, but not their dependence. This yields a sharp Frechet-Makarov set and motivates direct late calibration, dependence-robust transport, and signed or revision-model transport. Out-of-sample results support method choice rather than a universal transport rule: coverage and stability determine when transport gains are usable.
