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Forecasting Unemployment Rate Using a Neural Network with Fuzzy Inference System

Author

Listed:
  • George Atsalakis
  • Camelia Ioana Ucenic
  • Christos Skiadas

Abstract

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Suggested Citation

  • George Atsalakis & Camelia Ioana Ucenic & Christos Skiadas, 2008. "Forecasting Unemployment Rate Using a Neural Network with Fuzzy Inference System," Working Papers 0823, University of Crete, Department of Economics.
    • Handle: RePEc:crt:wpaper:0823
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    File URL: http://economics.soc.uoc.gr/wpa/docs/0823.pdf
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    Cited by:

    1. Tanujit Chakraborty & Ashis Kumar Chakraborty & Munmun Biswas & Sayak Banerjee & Shramana Bhattacharya, 2021. "Unemployment Rate Forecasting: A Hybrid Approach," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 183-201, January.
    2. Adriana AnaMaria Davidescu & Simona-Andreea Apostu & Liviu Adrian Stoica, 2021. "Socioeconomic Effects of COVID-19 Pandemic: Exploring Uncertainty in the Forecast of the Romanian Unemployment Rate for the Period 2020–2023," Sustainability, MDPI, vol. 13(13), pages 1-22, June.
    3. Mihai Mutascu & Scott W. Hegerty, 2023. "Predicting the contribution of artificial intelligence to unemployment rates: an artificial neural network approach," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 47(2), pages 400-416, June.

    More about this item

    Keywords

    forecasting; neural network; unemployment;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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