Projection estimators for autoregressive panel data models
提出一种基于投影未观测个体效应的新估计方法，用于自回归面板数据模型，得到与已知GMM估计量一致的估计量，并给出一个简单线性估计量，模拟表现良好。
In this paper we explore a new approach to estimation for autoregressive panel data models, based on projecting the unobserved individual effects on the vector of observations on the lagged dependent variable. This approach yields estimators which coincide with known generalized method of moments estimators for models where stationarity is not imposed on the initial conditions and for models which satisfy mean stationarity. Our approach allows us to obtain a simple linear estimator for models which satisfy covariance stationarity, which although not fully efficient performs very well in simulations.