发表机构
Carnegie Mellon University; Indiana University（卡内基梅隆大学; 印第安纳大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种概率模型发现方法，将时间序列分解为季节性成分，返回其后验分布，在模拟和COVID-19衰退期间美国宏观数据中优于X-13ARIMA-SEATS，并提前揭示季节性调整的不确定性。
AI 中文摘要
季节性调整是经济分析的基础，但由于季节性成分本质上是潜在的，因此具有不确定性。本文引入了一种概率模型发现方法，将时间序列分解为季节性成分和非季节性成分。该方法返回季节性成分结构和参数的后验分布。在模拟研究中，相对于X-13ARIMA-SEATS，该方法能够改进点预测、区间预测以及季节性成分的恢复。在一项针对COVID-19衰退期间八个美国宏观经济序列的研究中，该方法在实时中揭示了当前季节性调整存在显著的先验不确定性，远在许多X-13修订达到其最终峰值之前。
英文摘要
Seasonal adjustment is fundamental to economic analysis, but uncertain because seasonal components are inherently latent. This article introduces a probabilistic model discovery method that decomposes a time series into seasonal and nonseasonal components. The method returns a posterior distribution over the structure and parameters of a seasonal component. In simulation studies, the method can improve point forecasts, interval predictions, and recovery of seasonal components relative to X-13ARIMA-SEATS. In a study of eight U.S. macroeconomic series during the COVID-19 recession, the method surfaces significant ex-ante uncertainty about current seasonal adjustments in real time, well before many X-13 revisions reach their eventual peaks.
