Hidden group patterns in democracy developments: Bayesian inference for grouped heterogeneity
提出一种非参数贝叶斯方法，用于估计线性面板数据模型中随时间变化的分组异质性模式，可同时选择最优组数并量化不确定性，在蒙特卡洛模拟中表现良好，并成功复现了收入与民主关系的估计结果。
Summary We propose a nonparametric Bayesian approach to estimate time‐varying grouped patterns of heterogeneity in linear panel data models. Unlike the classical approach in Bonhomme and Manresa ( Econometrica , 2015, 83 , 1147–1184), our approach can accommodate selection of the optimal number of groups and model estimation jointly, and also be readily extended to quantify uncertainties in the estimated group structure. Our proposed approach performs well in Monte Carlo simulations. Using our approach, we successfully replicate the estimated relationship between income and democracy in Bonhomme and Manresa and the group characteristics when we use the same number of groups. Furthermore, we find that the optimal number of groups could depend on model specifications on heteroskedasticity and discuss ways to choose models in practice.