Yaya, OlaOluwa S.
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Ogbonna, Ahamuefula E.
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Furuoka, Fumitaka
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Gil Alana, Luis A.
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600
2022-06-13T10:18:16Z
2022-06-13T10:18:16Z
2021
0305-9049
http://hdl.handle.net/10641/3003
10.1111/obes.12422
This paper proposes a nonlinear unit root test based on the autoregressive neural network process for testing unemployment hysteresis. In this new unit root testing framework, the linear, quadratic and cubic components of the neural network process are used to capture the nonlinearity in a given time series data. The theoretical properties of the test are developed, while the size and the power properties are examined in a Monte Carlo simulation study. Various empirical applications with unemployment and inflation rates across a number of countries are carried out at the end of the article.
pre-print
437 KB
eng
Oxford Bulletin of Economics and Statistics
AtribuciĆ³n-NoComercial-SinDerivadas 3.0 EspaƱa
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
openAccess
Autoregressive Neural Network
Fractional integration
Unit root test
Non-linearity
A New Unit Root Test for Unemployment Hysteresis Based on the Autoregressive Neural Network.
article
https://onlinelibrary.wiley.com/doi/abs/10.1111/obes.12422
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Testing Fractional Integration with ARNN_OlaOluwa (12 february)(a).pdf
Testing Fractional Integration with ARNN_OlaOluwa (12 february)(a).pdf
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Testing Fractional Integration with ARNN_OlaOluwa (12 february)(a).pdf.txt
Testing Fractional Integration with ARNN_OlaOluwa (12 february)(a).pdf.txt
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