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### 截面相关面板数据的部分异质结构的识别

• 出版日期:2018-03-25 发布日期:2018-03-25

### Identifying Heterogeneity in the Partial Structure of Panel Data with Cross-Sectional Dependence

Xu Feng & Li Shi

• Online:2018-03-25 Published:2018-03-25

Abstract: Partial heterogeneity often exists in large panels and are characterized by group structure in which the parameters are constant within group and vary between groups. Neglecting this group structure may lead to inconsistent estimates and invalid inferences. Up till now, Researchers have not fully investigated partial heterogeneity in panel data models, for example partial heterogeneity in panel data with strong or non-strong factors. In view of this, we try to study the identification of unknown partially heterogeneous structure under a quite general framework with strong and semi-strong factors（Reese and Westerlund, 2015）. The method is proved to achieve uniformly consistent classification regardless of strong or semi-strong factors. Whereas the Lasso estimators and post-Lasso estimators converge fast to zero-mean normal distribution with the rise in the strength of factors. Simulation results indicate that the proposed method performs well under finite samples. Specifically, with the rise of N and T, the accuracy for classification and group numbers increases to 100% quickly, and the root-mean-square error and bias for the two types of estimators decrease significantly. Finally, an empirical analysis of Human Capital-Growth nexus is presented.