A New unit root test for unemployment hysteresis based on the autoregressive neural network*

Yaya, OlaOluwa S. and Ogbonna, Ahamuefula E. and Furuoka, Fumitaka and Gil-Alana, Luis A. (2021) A New unit root test for unemployment hysteresis based on the autoregressive neural network*. Oxford Bulletin of Economics and Statistics, 83 (4). pp. 960-981. ISSN 0305-9049, DOI https://doi.org/10.1111/obes.12422.

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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.

Item Type: Article
Funders: Spanish Government European Commission[ECO2017-85503-R], Universidad Francisco de Vitoria, Tun Ismail Ali Chair Research Grant[TIACRG2018.23]
Uncontrolled Keywords: Neural network ; Monte Carlo simulation ; Mathematical Methods
Subjects: Q Science > QA Mathematics
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Deputy Vice Chancellor (Academic & International) Office > Asia-Europe Institute
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 15 Apr 2022 02:42
Last Modified: 15 Apr 2022 02:42
URI: http://eprints.um.edu.my/id/eprint/26780

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