Furuoka, Fumitaka and Gil-Alana, Luis A. and Yaya, OlaOluwa S. and Aruchunan, Elayaraja and Ogbonna, Ahamuefula E. (2024) A new fractional integration approach based on neural network nonlinearity with an application to testing unemployment hysteresis. Empirical Economics, 66 (6). pp. 2471-2499. ISSN 0377-7332, DOI https://doi.org/10.1007/s00181-023-02540-5.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Funders: | Ministerio de Ciencia e Innovacin |
| Uncontrolled Keywords: | Autoregressive neural network; Fractional integration; Hysteresis; Unemployment; C15; C22; C45 |
| Subjects: | H Social Sciences > HD Industries. Land use. Labor |
| Divisions: | Deputy Vice Chancellor (Academic & International) Office > Asia-Europe Institute Faculty of Business and Economics |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 12 Nov 2024 07:26 |
| Last Modified: | 12 Nov 2024 07:26 |
| URI: | http://eprints.um.edu.my/id/eprint/45825 |
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