Higher-Order Expansions and Inference for Panel Data Models
提出一种适用于同时存在序列相关和截面依赖的面板数据模型的简单推断方法，通过发展Berry-Esseen界和Edgeworth展开等新高阶展开理论来支撑该方法，并用交互效应模型验证其有效性。
In this article, we propose a simple inferential method for a wide class of panel data models with a focus on such cases that have both serial correlation and cross-sectional dependence.In order to establish an asymptotic theory to support the inferential method, we develop some new and useful higher-order expansions, such as Berry-Esseen bound and Edgeworth Expansion, under a set of simple and general conditions.We further demonstrate the usefulness of these theoretical results by explicitly investigating a panel data model with interactive effects which nests many traditional panel data models as special cases.Finally, we show the superiority of our approach over several natural competitors using extensive numerical studies.