A New Unit Root Test for Unemployment Hysteresis Based on the Autoregressive Neural Network*
提出一种基于自回归神经网络过程的非线性单位根检验方法，用于检验失业滞后性，通过蒙特卡洛模拟考察检验性质，并应用于多国失业率和通胀率数据。
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.