Testing Models of Low-Frequency Variability
提出一个框架，通过计算原始数据的低频加权平均来提取低频信息，并与常见时间序列模型的渐近性质比较，用于评估模型对低频变异性的解释能力，并应用于20个美国宏观经济和金融时间序列。
We develop a framework to assess how successfully standard time series models explain low-frequency variability of a data series. The low-frequency information is extracted by computing a finite number of weighted averages of the original data, where the weights are low-frequency trigonometric series. The properties of these weighted averages are then compared to the asymptotic implications of a number of common time series models. We apply the framework to twenty U.S. macroeconomic and financial time series using frequencies lower than the business cycle. Copyright 2008 The Econometric Society.