论文标题
面板数据模型的高阶扩展和推断
Higher-order Expansions and Inference for Panel Data Models
论文作者
论文摘要
在本文中，我们为广泛的面板数据模型提出了一种简单的推论方法，其重点是具有串行相关和横截面依赖性的情况。为了建立一种渐近理论来支持推论方法，我们在一组简单且一般的条件下开发了一些新的和有用的高阶扩展，例如Berry-Esseen Bound和Edgeworth的扩展。我们通过明确调查具有交互式效应的面板数据模型，进一步证明了这些理论结果的有用性，该模型将许多传统面板数据模型嵌套为特殊情况。最后，我们使用广泛的数值研究表明了方法比几个自然竞争对手的优越性。
In this paper, 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.
