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Testing Models of Low-Frequency Variability

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Author Info
Ulrich Mueller
Mark W. Watson
Abstract

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.

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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 12671.

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Date of creation: Nov 2006
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Handle: RePEc:nbr:nberwo:12671

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Find related papers by JEL classification:
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Arturo Estrella, 2007. "Extracting business cycle fluctuations: what do time series filters really do?," Staff Reports 289, Federal Reserve Bank of New York. [Downloadable!]
  2. C.S. Bos & S.J. Koopman & M. Ooms, 2007. "Long Memory Modelling of Inflation with Stochastic Variance and Structural Breaks," Tinbergen Institute Discussion Papers 07-099/4, Tinbergen Institute. [Downloadable!]
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