Testing models of low-frequency variability

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56 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)979-1016
Number of pages38
Issue number5
StatePublished - Sep 2008

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics


  • Business cycle frequency
  • Heteroskedasticity
  • Local-to-unity
  • Long memory
  • Stationarity test
  • Unit root test


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