Bayesian treatment of the independent student-t linear model
提出在线性模型中用贝叶斯方法处理独立同分布的学生t分布扰动项，利用正态尺度混合和吉布斯采样进行计算，并应用于宏观经济时间序列，发现后验优势比支持学生t模型优于正态模型，且对差分平稳性的支持减弱。
This article takes up methods for Bayesian inference in a linear model in which the disturbances are independent and have identical Student-t distributions. It exploits the equivalence of the Student-t distribution and an appropriate scale mixture of normals, and uses a Gibbs sampler to perform the computations. The new method is applied to some well-known macroeconomic time series. It is found that posterior odds ratios favour the independent Student-t linear model over the normal linear model, and that the posterior odds ratio in favour of difference stationarity over trend stationarity is often substantially less in the favoured Student-t models.