Non-parametric Panel Data Models with Interactive Fixed Effects

• 成果类型: Article
• 署名作者: Freyberger, Joachim
• 署名单位: University of Wisconsin System; University of Wisconsin Madison
• 刊物名称: REVIEW OF ECONOMIC STUDIES
• ISSN/ISSBN: 0034-6527
• DOI: 10.1093/restud/rdx052
• 发表日期: 2018
• 页码: 1824-1851
• 关键词: instrumental variables; student-achievement; nonlinear models; cross-section; identification; deconvolution; TECHNOLOGY; regression; average; number

摘要

This article studies non-parametric panel data models with multidimensional, unobserved individual effects when the number of time periods is fixed. I focus on models where the unobservables have a factor structure and enter an unknown structural function non-additively. The setup allows the individual effects to impact outcomes differently in different time periods and it allows for heterogeneous marginal effects. I provide sufficient conditions for point identification of all parameters of the model. Furthermore, I present a non-parametric sieve maximum likelihood estimator as well as flexible semiparametric and parametric estimators. Monte Carlo experiments demonstrate that the estimators perform well in finite samples. Finally, in an empirical application, I use these estimators to investigate the relationship between teaching practice and student achievement. The results differ considerably from those obtained with commonly used panel data methods.