Change point analysis has become an important research topic in many fields of applications. Several research work have been carried out to detect changes and its locations in time series data. In this paper, a nonparametric method based on the empirical likelihood is proposed to detect structural changes in the parameters of autoregressive (AR) models . Under certain conditions, the asymptotic null distribution of the empirical likelihood ratio test statistic is proved to be Gumbel type. Further, the consistency of the test statistic is verified. Simulations are carried out to show that the power of the proposed test statistic is significant. The proposed method is applied to monthly average soybean sales data to further illustrate the testing procedure.

变更点分析已成为许多应用程序领域中的重要研究主题。已经进行了一些研究工作来检测时间序列数据中的变化及其位置。本文提出了一种基于经验似然性的非参数方法来检测自回归（AR）模型参数的结构变化。在某些条件下，经验似然比检验统计量的渐近零分布被证明是Gumbel类型。此外，验证了检验统计量的一致性。仿真表明，所提出的检验统计量的功效是显着的。所提出的方法应用于月平均大豆销售数据，以进一步说明测试程序。