A Bayesian Significance Test of the Stationarity of Regression Parameters
提出一种基于最高后验密度可信集的贝叶斯显著性检验，用于判断回归方程是否平稳，并通过蒙特卡洛模拟证明其检验效力优于Cusum和Cusum平方检验，适用于检测单个参数的非平稳性。
This paper presents a Bayesian significance test for stationarity of a regression equation using the highest posterior density credible set. In addition, a solution to the Behrens-Fisher problem is provided. From a Monte Carlo simulation study, it has been shown that the Bayesian significance test has stronger power than the Cusum and the Cusum of squares tests. The Bayesian significance test may be useful in detecting individual parameter nonstationarity.