Estimation and inference for multi-dimensional heterogeneous panel datasets with hierarchical multi-factor error structure
提出了三维面板数据模型，通过扩展共同相关效应估计方法处理分层因子结构，并提供了渐近理论和非参数方差估计量，蒙特卡洛模拟和贸易出口数据应用验证了其有效性。
Given the growing availability of large datasets and following recent research trends on multi-dimensional modelling, we develop three dimensional (3D) panel data models with hierarchical error components that allow for strong cross-section dependence through unobserved heterogeneous global and local factors. We propose consistent estimation procedures by extending the common correlated effects (CCE) estimation approach proposed by Pesaran (2006). The standard CCE approach needs to be modified in order to account for the hierarchical factor structure in 3D panels. Further, we provide asymptotic theory, including new nonparametric variance estimators. The validity of the proposed approach is confirmed by Monte Carlo simulation studies. We demonstrate the empirical usefulness of the proposed framework through an application to a 3D panel gravity model of bilateral export flows.