关键词: 面板协整, 分位回归, 公共因子, MCMC, 贝叶斯分析

Abstract: The traditional panel cointegration methods are based on conditional mean models, which is relatively sensitive to outliers. Meanwhile, the cointegration results may differ when the null hypothesis varies, which causes the difficulty of the cointegration tests in the case of different null hypothesis. This paper proposes Bayesian quantile panel cointegration method on the basis of the panel data dynamic factor model, which implements dynamic common factor to characterize the possible cross-section dependence in panel data. To get the parameter estimates of the model and conduct Bayesian quantile panel cointegration test in the major quantiles, the Gibbs sampling algorithms are designed. We also conduct a Monte Carlo study to illustrate the feasibility and validity of Bayesian quantile panel cointegration. Finally, through the empirical application of Chinese financial development and economic growth, we find there are cointegration relationships between Chinese financial development and economic growth in the major quantiles. It shows that Bayesian quantile cointegration method avoids the bias caused by the set of null hypothesis in the traditional panel cointegration and tends to be impervious to outliers. It also shows that Bayesian quantile panel cointegration can provide full and exact information on parameter estimation and cointegration tests.

Key words: panel cointegration, quantile regression, common factor, MCMC, Bayesian analysis