Efficiency of QMLE for Dynamic Panel Data Models with Interactive Effects

• 成果类型: Article
• 署名作者: Bai, Jushan
• 署名单位: Columbia University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2025.2552452
• 发表日期: 2026-04-03
• 页码: 1499-1510
• 关键词: Efficiency bound; factor models; fixed effects; Incidental parameters; Local likelihood ratios; Local parameter space; Regular estimators; inference

摘要

This article studies the problem of efficient estimation of panel data models in the presence of an increasing number of incidental parameters. We formulate the dynamic panel as a simultaneous equations system, and derive the efficiency bound under the normality assumption. We then show that the Gaussian quasi-maximum likelihood estimator (QMLE) applied to the system achieves the efficiency bound without the normality assumption. Comparison of QMLE with the fixed effects approach is made. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.