JSM 2004 - Toronto

Abstract #301227

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Activity Number: 308
Type: Contributed
Date/Time: Wednesday, August 11, 2004 : 8:30 AM to 10:20 AM
Sponsor: Business and Economics Statistics Section
Abstract - #301227
Title: Identifying the Cycle of a Macroeconomic Time Series Using Fuzzy Filtering
Author(s): David E. Giles*+ and Chad N. Stroomer
Companies: University of Victoria and University of Victoria
Address: PO Box 1700, STN CSC, Victoria, BC, V8W2Y2, Canada
Keywords: business cycle ; fuzzy sets ; Hodrick-Prescott filter ; fuzzy logic
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 subsamples, 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 nonlinearities 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 an extensive Monte Carlo experiment, and the new filter is found to perform as well as, or better than, the HP filter over a wide range of time-series characterstics. The paper also includes some applications with real time series to illustrate the different conclusions that can emerge when the fuzzy filter and the HP filter are each applied to extract the cycle.

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