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

Fumitaka Furuoka, Luis Gil-Alana, Olaoluwa Yaya, Elayaraja Aruchunan () and Ahamuefula Ogbonna
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Elayaraja Aruchunan: Universiti Malaya

Empirical Economics, 2024, vol. 66, issue 6, No 5, 2499 pages

Abstract: 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.

Keywords: Autoregressive neural network; Fractional integration; Hysteresis; Unemployment (search for similar items in EconPapers)
JEL-codes: C15 C22 C45 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00181-023-02540-5

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