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A New Unit Root Test for Unemployment Hysteresis Based on the Autoregressive Neural Network*
Author(s) -
Yaya OlaOluwa S.,
Ogbonna Ahamuefula E.,
Furuoka Fumitaka,
GilAlana Luis A.
Publication year - 2021
Publication title -
oxford bulletin of economics and statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.131
H-Index - 73
eISSN - 1468-0084
pISSN - 0305-9049
DOI - 10.1111/obes.12422
Subject(s) - unit root , autoregressive model , unit root test , artificial neural network , inflation (cosmology) , nonlinear system , econometrics , hysteresis , series (stratigraphy) , unemployment , monte carlo method , quadratic equation , computer science , process (computing) , augmented dickey–fuller test , economics , mathematics , statistics , artificial intelligence , macroeconomics , paleontology , physics , geometry , cointegration , quantum mechanics , theoretical physics , biology , operating system
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.