Testing Models of Low-Frequency Variability
We develop a framework to assess how successfully standard times eries 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.
Published: Müller, Ulrich K. and Mark W. Watson. "Testing Models of Low-Frequency Variability." Econometrica 76, 5 (2008): 979-1016.