Testing Models of Low-Frequency Variability

Econometrica · 2008 · Volume 76 · Issue 5

Abstract

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.

Keywords

Long memory, local-to-unity, unit root test, stationarity test, business cycle frequency, heteroskedasticity

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