Efficiency of QMLE for Dynamic Panel Data Models with Interactive Effects
研究了存在大量附带参数时面板数据模型的有效估计问题，将动态面板构建为联立方程组系统，推导了正态假设下的效率界，并证明高斯拟极大似然估计量无需正态假设即可达到该效率界。
This paper 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.