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

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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.

Suggested Citation

  • David E. Giles & Chad N. Stroomer, 2004. "Identifying the Cycle of a Macroeconomic Time-Series Using Fuzzy Filtering," Econometrics Working Papers 0406, Department of Economics, University of Victoria.
    • Handle: RePEc:vic:vicewp:0406
    Note: ISSN 1485-6441
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    File URL: https://www.uvic.ca/socialsciences/economics/_assets/docs/econometrics/ewp0406.pdf
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    References listed on IDEAS

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    Cited by:

    1. Hui Feng & David E. Giles, 2007. "Bayesian Fuzzy Regression Analysis and Model Selection: Theory and Evidence," Econometrics Working Papers 0710, Department of Economics, University of Victoria.
    2. Shepherd, David & Shi, Francis K.C., 2006. "Fuzzy modelling and estimation of economic relationships," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 417-433, November.
    3. Erfani , Alireza & Safari , Solmaz, 2014. "Estimation of Seigniorage Laffer curve in Iran: A Fuzzy C-Means Clustering Framework," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 9(1), pages 93-115, October.
    4. Hui Feng, 2011. "Forecasting comparison between two nonlinear models: fuzzy regression versus SETAR," Applied Economics Letters, Taylor & Francis Journals, vol. 18(17), pages 1623-1627.
    5. knani, ramzi & fredj, ali, 2010. "Mondialisation et fluctuations des cycles économiques [globalisation and business cycle fluctuation]," MPRA Paper 22755, University Library of Munich, Germany.

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    More about this item

    Keywords

    Fuzzy filter; fuzzy clustering; business cycle; trend extraction; HP filter;
    All these keywords.

    JEL classification:

    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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