Higher-Order Expansions and Inference for Panel Data Models

• 成果类型: Article
• 署名作者: Gao, Jiti; Peng, Bin; Yan, Yayi
• 署名单位: Monash University; Shanghai University of Finance & Economics
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2023.2277411
• 发表日期: 2024
• 页码: 2760-2771
• 关键词: Bootstrap; heteroskedasticity; dependence; network

摘要

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. Supplementary materials for this article are available online.