Bayesian Inference for Multiple Change Points in Time Series-Demonstration Based on Chinese GDP Series
In this article, a basic Bayesian analysis is introduced to detect multiple change points in time series firstly. The Bayes factor is used to judge the number of change points. Then, GDP series is analyzed based on this theory and Gibbs sample is carried out by dint of WinBUGS software. There are three change points in GDP series, which happened in 1961, 1976, 1989 respectively. Its general in agreement with our countrys economic seedtime. At last, it is familiar that the model with inserting proper change points is much better in forecast.
Bayesian Inference Multiple Change Points GDP
WANG Wei Guo WANG Xia YAN Min
School of Mathematics & Econometrics, Dongbei University of Finance and Economics
国际会议
2008年国际应用统计学术研讨会(2008 International Institute of Applied Statistics Studies)
烟台
英文
1-6
2008-08-14（万方平台首次上网日期，不代表论文的发表时间）