Identifying the Cycle of a Macroeconomic Time-Series Using Fuzzy Filtering

David Giles and Chad N. Stroomer ()
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Chad N. Stroomer: Department of Economics, University of Victoria, https://www.uvic.ca/socialsciences/economics/

No 406, Econometrics Working Papers from Department of Economics, University of Victoria

Abstract: This paper presents a new method for extracting the cycle from an economic time series. This method uses the fuzzy c-means clustering algorithm, drawn from the pattern recognition literature, to identify groups of observations. The time series is modeled over each of these sub-samples, and the results are combined using the “degrees of membership” for each data-point with each cluster. The result is a totally flexible model that readily captures complex non-linearities in the data. This type of “fuzzy regression” analysis has been shown by Giles and Draeseke (2003) to be highly effective in a broad range of situations with economic data. The fuzzy filter that we develop here is compared with the well-known Hodrick-Prescott (HP) filter in a Monte Carlo experiment, and the new filter is found to perform as well as, or better than, the HP filter. The advantage of the fuzzy filter is especially pronounced when the data have a deterministic, rather than stochastic, trend. Applications with real time-series illustrate the different conclusions that can emerge when the fuzzy regression filter and the HP filter are each applied to extract the cycle.

Keywords: Fuzzy filter; fuzzy clustering; business cycle; trend extraction; HP filter (search for similar items in EconPapers)
JEL-codes: C19 C22 E32 (search for similar items in EconPapers)
Pages: 24 pages
Date: 2004-12-29
New Economics Papers: this item is included in nep-cmp, nep-ecm, nep-ets and nep-mac
Note: ISSN 1485-6441
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:vic:vicewp:0406

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