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A new unit root analysis for testing hysteresis in unemployment

Author

Listed:
  • Yaya, OlaOluwa S
  • Ogbonna, Ephraim A
  • Furuoka, Fumitaka
  • Gil-Alana, Luis A.
Abstract
This paper proposes a nonlinear unit root test based on the artificial neural network-augmented Dickey-Fuller (ANN-ADF) test for testing hysteresis in unemployment. In this new unit root test, the linear, quadratic and cubic components of the neural network process are used to capture the nonlinearity in the time-series data. Fractional integration methods, based on linear and nonlinear trends are also used in the paper. By considering five European countries such as France, Italy, Netherland, Sweden, and the United Kingdom, the empirical findings indicate that there is still hysteresis in these countries. Among batteries of unit root tests applied, both the ARNN-ADF and fractional integration tests fail to reject the hypothesis of unemployment hysteresis in all the countries.

Suggested Citation

  • Yaya, OlaOluwa S & Ogbonna, Ephraim A & Furuoka, Fumitaka & Gil-Alana, Luis A., 2019. "A new unit root analysis for testing hysteresis in unemployment," MPRA Paper 96621, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:96621
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    File URL: https://mpra.ub.uni-muenchen.de/96621/1/MPRA_paper_96621.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    Unit root process; Nonlinearity; Neuron network: Time-series; Hysteresis; Unemployment; Europe; Labour market.;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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