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.

中文翻译：

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基于自回归神经网络的失业滞后的新单位根检验*

本文提出了一种基于自回归神经网络过程的非线性单位根检验，用于检验失业滞后现象。在这个新的单位根测试框架中，神经网络过程的线性、二次和三次分量用于捕捉给定时间序列数据中的非线性。开发了测试的理论特性，同时在蒙特卡罗模拟研究中检查了大小和功率特性。文章末尾对多个国家的失业率和通货膨胀率进行了各种实证应用。