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.