Detecting homogenous predictors in high-dimensional panel model with an MCMC algorithm.

In panel data analysis, predictors may impact response in substantially different manner. Some predictors are in homogenous effects across all individuals, while the others are in heterogenous way. How to effectively differentiate these two kinds of predictors is crucial, particularly in high-dimensional panel data, since the number of parameters should be greatly reduced and hence lead to better interpretability by homogenous assumption. In this article, based on a hierarchical Bayesian panel regression model, we propose a novel yet effective Markov chain Monte Carlo (MCMC) algorithm together with a simple maximum ratio criterion to detect the predictors in homogenous effects in high-dimensional panel data. Extensive Monte Carlo simulations show that this MCMC algorithm performs well. The usefulness of the proposed method is further demonstrated by a real example from China financial market.