具有多层多因素误差结构的多维异构面板数据集的估计和推断
Estimation and Inference for Multi-dimensionalHeterogeneous Panel Datasets with HierarchicalMulti-factor Error Structure
作者:
乔治·卡佩塔尼奥斯(George Kapetanios)
劳拉·塞伦加(Laura Serlenga)
辛che哲(Yongcheol Shin)
Given the growing availability of large datasets and following recent research trendson multi-dimensional modelling, we develop three dimensional (3D) panel data models with hierarchical error components that allow for strong cross-sectional dependencethrough unobserved heterogeneous global and local factors. We propose consistent estimation procedures by extending the common correlated e§ects (CCE) estimation approach proposed by Pesaran (2006). The standard CCE approach needs to be modifiedin order to account for the hierarchical factor structure in 3D panels. Further, we provide the associated asymptotic theory, including new nonparametric variance estimators.The validity of the proposed approach is confirmed by Monte Carlo simulation studies.We also demonstrate the empirical usefulness of the proposed approach through an application to a 3D panel gravity model of bilateral export flows.
鉴于大型数据集的可用性不断增长,并且随着多维模型的最新研究趋势,我们开发了具有分层误差分量的三维(3D)面板数据模型,这些分量允许通过未观察到的异质全局和局部因素来实现强烈的截面依赖性。我们通过扩展Pesaran(2006)提出的公共相关效应(CCE)估计方法,提出一致的估计程序。为了考虑3D面板中的分层因子结构,需要修改标准CCE方法。此外,我们提供了相关的渐近理论,包括新的非参数方差估计量。蒙特卡罗模拟研究证实了该方法的有效性。