Bayesian multiple structural change-points estimation in time series models with genetic algorithm

This article considers a time series model with a deterministic trend, in which multiple structural changes are explicitly taken into account, while the number and the location of change-points are unknown. We aim to figure out the best model with the appropriate number of change-points and a certain length of segments between points. We derive a posterior probability and then apply a genetic algorithm (GA) to calculate the posterior probabilities to locate the change-points. GA results in a powerful flexible tool which is shown to search over possible change-points. Numerical results obtained from simulation experiments show excellent empirical properties. To verify our model retrospectively, we estimate structural change-points with US and South Korean GDP data.