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A New Unit Root Test for Unemployment Hysteresis Based on the Autoregressive Neural Network.

Identificadores del recurso

0305-9049

http://hdl.handle.net/10641/3003

10.1111/obes.12422

Origin

(Repositorio Institucional de la Universidad Francisco de Vitoria)

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Title:: A New Unit Root Test for Unemployment Hysteresis Based on the Autoregressive Neural Network.
Tema:: Autoregressive Neural Network; Fractional integration; Unit root test; Non-linearity
Description:: 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
Idioma:: English
Relation:: https://onlinelibrary.wiley.com/doi/abs/10.1111/obes.12422
Autor/Productor:: Yaya, OlaOluwa S.; Ogbonna, Ahamuefula E.; Furuoka, Fumitaka; Gil Alana, Luis A.
Publisher:: Oxford Bulletin of Economics and Statistics
Rights:: Atribución-NoComercial-SinDerivadas 3.0 España; http://creativecommons.org/licenses/by-nc-nd/3.0/es/; openAccess
Date:: 2022-06-13T10:18:16Z; 2021
Tipo de recurso:: article

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        <field name="value">Yaya, OlaOluwa S.</field>
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        <field name="value">Ogbonna, Ahamuefula E.</field>
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        <field name="value">Gil Alana, Luis A.</field>
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        <field name="value">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.</field>
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