Non-parametric Panel Data Models with Interactive Fixed Effects
研究了时间期数固定时含多维不可观测个体效应的非参数面板数据模型，提出识别条件和多种估计量，蒙特卡洛实验表现良好，实证分析发现教学方法与学生成绩的关系与常用方法结果不同。
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.