Maximum likelihood estimation of change point from stationary to nonstationary in autoregressive models using dynamic linear model |
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| Authors: | Reza Sheikhrabori Majid Aminnayeri Mona Ayoubi |
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| Affiliation: | 1. Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran;2. Department of Industrial Engineering, College of Engineering, West Tehran Branch, Islamic Azad University, Tehran, Iran |
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| Abstract: | Change point estimation is a useful concept in time series models that could be applied in several fields such as financing, quality control. It helps to decrease costs of decision making and production by monitoring stock market and production lines, respectively. In this paper, the maximum likelihood technique is developed to estimate change point at which the stationary AR(1) model changes to a nonstationary process. Filtering and smoothing of dynamic linear model are used to estimate unknown parameters after change point. We also assume that correlation exists between samples' statistics. Simulation results show the effectiveness of the proposed estimators to estimate the change point of stationary. In addition based on Shewhart control chart, filtering has a better accuracy in comparison to smoothing. A real example is provided to illustrate the application. |
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| Keywords: | AR(1) model change point estimation dynamic linear model (DLM) maximum likelihood estimation time series |
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