A ROBUST BAYESIAN APPROACH FOR UNIT ROOT TESTING
研究如何识别时间序列的自回归模型并检测其特征多项式中的单位根，重点探讨标准贝叶斯分析对自回归系数先验分布选择的敏感性，提出一种稳健的贝叶斯方法。
In this paper we deal with the identification of an autoregressive model for an observed time series and the detection of a unit root in its characteristic polynomial. This is a big issue concerned with distinguishing stationary time series from time series for which differencing is required to induce stationarity. We consider a Bayesian approach, and particular attention is devoted to the problem of the sensitivity of the standard Bayesian analysis with respect to the choice of the prior distribution for the autoregressive coefficients.We thank three anonymous referees for their useful comments, which have improved the final version of the paper.