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The Forecasting of Labour Force Participation and the Unemployment Rate in Poland and Turkey Using Fuzzy Time Series Methods

Author

Listed:
  • Yolcu Ufuk

    (Ph.D. Associate Professor at Ankara University, Faculty of Sciences, Department of Statistics)

  • Bas Eren

    (Ph.D. Assistant Professor at Giresun University, Faculty of Arts and Sciences, Department of Statistics)

Abstract

Fuzzy time series methods based on the fuzzy set theory proposed by Zadeh (1965) was first introduced by Song and Chissom (1993). Since fuzzy time series methods do not have the assumptions that traditional time series do and have effective forecasting performance, the interest on fuzzy time series approaches is increasing rapidly. Fuzzy time series methods have been used in almost all areas, such as environmental science, economy and finance. The concepts of labour force participation and unemployment have great importance in terms of both the economy and sociology of countries. For this reason there are many studies on their forecasting. In this study, we aim to forecast the labour force participation and unemployment rate in Poland and Turkey using different fuzzy time series methods.

Suggested Citation

  • Yolcu Ufuk & Bas Eren, 2016. "The Forecasting of Labour Force Participation and the Unemployment Rate in Poland and Turkey Using Fuzzy Time Series Methods," Comparative Economic Research, Sciendo, vol. 19(2), pages 5-25, June.
    • Handle: RePEc:vrs:coecre:v:19:y:2016:i:2:p:5-25:n:1
    DOI: 10.1515/cer-2016-0010
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    References listed on IDEAS

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    1. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
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