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Forecasting comparison between two nonlinear models: fuzzy regression versus SETAR

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  • Hui Feng

Abstract

In this article, we compare the forecasting performances of the Self-Exciting Threshold Autoregressive (SETAR) model and a fuzzy clustering regression model. The series used in this study are high-frequency financial data in the form of seven major stock prices in the US stock markets; the stock indices from seven world stock trading centres; the daily prices for two important commodities, gold and crude oil; and the daily exchange rate between the Canadian dollar and the US dollar. We find that the two models are not too different from each other in terms of the within-sample fit, but in terms of the forecasting performance, the fuzzy model gives better and stable forecasts.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:apeclt:v:18:y:2011:i:17:p:1623-1627
    DOI: 10.1080/13504851.2011.554369
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    References listed on IDEAS

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    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. 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.
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    Cited by:

    1. Grabowski Daniel & Staszewska-Bystrova Anna & Winker Peter, 2017. "Generating prediction bands for path forecasts from SETAR models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(5), pages 1-18, December.

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