A new fractional integration approach based on neural network nonlinearity with an application to testing unemployment hysteresis
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 St...
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| Main Authors: | , , , , |
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| Format: | Article |
| Published: |
Springer
2024
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/45825/ https://doi.org/10.1007/s00181-023-02540-5 |
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| Summary: | 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. |
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