A Bayesian multiple structural change regression model with autocorrelated errors |
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Authors: | Jaehee Kim Chulwoo Jeong |
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Institution: | 1. Department of Statistics, Duksung Women's University, Seoul, South Korea;2. Korea Institute for Defense Analyses, Seoul, South Korea |
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Abstract: | This paper develops a new Bayesian approach to change-point modeling that allows the number of change-points in the observed autocorrelated times series to be unknown. The model we develop assumes that the number of change-points have a truncated Poisson distribution. A genetic algorithm is used to estimate a change-point model, which allows for structural changes with autocorrelated errors. We focus considerable attention on the construction of autocorrelated structure for each regime and for the parameters that characterize each regime. Our techniques are found to work well in the simulation with a few change-points. An empirical analysis is provided involving the annual flow of the Nile River and the monthly total energy production in South Korea to lead good estimates for structural change-points. |
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Keywords: | Autoregressive process Bayesian time series model with multiple structural changes BIC posterior genetic algorithm truncated Poisson |
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