Testing Models of Low-Frequency Variability

Joint with: Ulrich K. Müller Princeton University

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-one U.S. macroeconomic and financial time series using frequencies lower than the business cycle.

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