Purpose: Sometimes the performance of a process is better analyzed by panel data rather than measurements on only a time series. When a change manifests itself in the cross-section(s) of panel data, detection of the real-time change corresponding to the panel data leads practitioners to identify the responsible factor(s) that affected the distribution of the panel. This invaluable time analysis is referred to as the change point. This paper proposes a new effective method to identify the change point of the panel data case.Design/methodology/approach: Considering cross-sectional time series, the identification of the change point of panel data has been evaluated. Two real cases have been studied to analyze the performance of the proposed method.Findings: The comparative numerical performance analysis of different simulated cases indicates that the proposed method is more effective compared to the methods proposed in the literature.Practical implications: The investigation of two real case studies in this paper addresses that the proposed method allows practitioners to use the proposed applied approach for analyzing different real panel cases.
