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

Olaoluwa Yaya, Ahamuefula Ogbonna, Fumitaka Furuoka and Luis A. Gil‐Alana
Authors registered in the RePEc Author Service: Luis Alberiko Gil-Alana

Oxford Bulletin of Economics and Statistics, 2021, vol. 83, issue 4, 960-981

Abstract: 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.

Date: 2021
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