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A new fractional integration approach based on neural network nonlinearity with an application to testing unemployment hysteresis

Author

Listed:
  • Fumitaka Furuoka

    (Universiti Malaya)

  • Luis A. Gil-Alana

    (University of Navarra
    Universidad Francisco de Vitoria)

  • OlaOluwa S. Yaya

    (University of Ibadan
    Centre for Econometrics and Applied Research)

  • Elayaraja Aruchunan

    (Universiti Malaya)

  • Ahamuefula E. Ogbonna

    (University of Ibadan
    Centre for Econometrics and Applied Research)

Abstract

This paper proposes a nonlinear fractional unit root approach which is known as the autoregressive neural network–fractional integration (ARNN–FI) test. This new fractional integration test is based on a new multilayer perceptron of a neural network process, proposed in Yaya et al. (Oxf Bull Econ Stat 83(4):960–981, 2021). The asymptotic theory and the properties of the proposed test are given. By setting up a Monte Carlo simulation experiment, the simulation results reveal that as the number of observations increases, size and power distortions would disappear in the test. The empirical application based on this new test reveals that the unemployment rates of three European countries are neither stationary nor mean-reverting in line with the hysteresis hypothesis.

Suggested Citation

  • Fumitaka Furuoka & Luis A. Gil-Alana & OlaOluwa S. Yaya & Elayaraja Aruchunan & Ahamuefula E. Ogbonna, 2024. "A new fractional integration approach based on neural network nonlinearity with an application to testing unemployment hysteresis," Empirical Economics, Springer, vol. 66(6), pages 2471-2499, June.
    • Handle: RePEc:spr:empeco:v:66:y:2024:i:6:d:10.1007_s00181-023-02540-5
    DOI: 10.1007/s00181-023-02540-5
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    More about this item

    Keywords

    Autoregressive neural network; Fractional integration; Hysteresis; Unemployment;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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