Temporal Disaggregation: Methods, Information Loss, and Diagnostics
提出一个两阶段框架，先用线性回归利用相关变量，再用状态空间模型分解回归残差，从而将低频时间序列分解为高频，并评估分解效果。通过蒙特卡洛模拟和两个实证研究验证框架，同时测量时间聚合中的信息损失。
This research provides a generalized framework to disaggregate lower-frequency time series and evaluate the disaggregation performance. The proposed framework combines two models in separate stages: a linear regression model to exploit related independent variables in the first stage and a state--space model to disaggregate the residual from the regression in the second stage. For the purpose of providing a set of practical criteria for assessing the disaggregation performance, we measure the information loss that occurs during temporal aggregation while examining what effects take place when aggregating data. To validate the proposed framework, we implement Monte Carlo simulations and provide two empirical studies. Supplementary materials for this article are available online.