Tree-Structured Clustering in Fixed Effects Models
提出一种递归划分方法，在固定效应模型中识别具有相同效应的单位聚类，减少待估参数数量，尤其适用于异质性与解释变量相关时，表现优于有限混合模型。
Fixed effects models are very flexible because they do not make assumptions on the distribution of effects and can also be used if the heterogeneity component is correlated with explanatory variables. A disadvantage is the large number of effects that have to be estimated. A recursive partitioning (or tree based) method is proposed that identifies clusters of units that share the same effect. The approach reduces the number of parameters to be estimated and is useful in particular if one is interested in identifying clusters with the same effect on a response variable. It is shown that the method performs well and outperforms competitors like the finite mixture model in particular if the heterogeneity component is correlated with explanatory variables. In two applications the usefulness of the approach to identify clusters that share the same effect is illustrated. Supplementary materials for this article are available online.