A Bayesian Approach to Modeling Time-Varying Cointegration and Cointegrating Rank
提出一个允许协整矩阵和协整秩随时间变化的多元模型，用于检验协整关系是否恒定，并应用于评估费雪效应和股票市场数据。
A multivariate model that allows for both a time-varying cointegrating matrix and time-varying cointegrating rank is presented. The model addresses the issue that, in real data, the validity of a constant cointegrating relationship may be questionable. The model nests the submodels implied by alternative cointegrating matrix ranks and allows for transitions between stationarity and nonstationarity, and cointegrating and noncointegrating relationships in accordance with the observed behavior of the data. A Bayesian test of cointegration is also developed. The model is used to assess the validity of the Fisher effect and is also applied to equity market data.