Stochastic search selection for heterogeneous panel data models
提出一种在贝叶斯分层面板数据模型中同时选择变量和判断参数异质性的方法，通过混合分布先验和选择指标识别最优模型，并应用于美国CPI子指数通胀和都市区房价通胀两个数据集。
.This article presents a method for selecting variables and determining parameter heterogeneity in Bayesian hierarchical panel data models. Mixture distributions are used as priors for the mean and the variance of the individuals’ parameters. Selection indicators determine the best-fitting component of each mixture distribution and indicate whether the mean parameter is non zero and whether the parameters are heterogeneous. The method is applied to two panel data sets. The first is on inflation of US CPI sub-indices, and the results suggest that a heterogeneous panel AR model with a lagged, first principal component is the preferred model. A second application to house price inflation across US metropolitan statistical areas shows that the model includes either the autoregressive component or the lagged spatial components, but not both at the same time.