期刊
ECONOMETRICA
卷 76, 期 5, 页码 979-1016出版社
WILEY-BLACKWELL
DOI: 10.3982/ECTA6814
关键词
long memory; local-to-unity; unit root test; stationarity test; business cycle frequency; heteroskedasticity
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.
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