Assessing Macro Uncertainty in Real-Time When Data Are Subject To Revision
研究发现，当数据存在修正时，基于样本内拟合的传统不确定性估计会产生偏差，并提出一种简单修正方法，用于更准确地预测25个宏观变量的预测区间，模拟研究验证了其小样本性质。
Model-based estimates of future uncertainty are generally based on the in-sample fit of the model, as when Box-Jenkins prediction intervals are calculated. However, this approach will generate biased uncertainty estimates in real time when there are data revisions. A simple remedy is suggested, and used to generate more accurate prediction intervals for 25 macroeconomic variables, in line with the theory. A simulation study based on an empirically-estimated model of data revisions for US output growth is used to investigate small-sample properties.