Estimation of fixed effects dynamic panel data models: linear differencing or conditional expectation
比较了Mundlak和Chamberlain的条件均值方法与线性差分方法在估计固定效应动态面板数据模型时的优劣，通过分析和蒙特卡洛模拟展示了解释变量数据生成过程和初始值处理对估计无偏性的重要性。
This note discusses the pros and cons of using the conditional mean approach of Mundlak and Chamberlain and the linear difference approach to deal with the incidental parameters issue in estimating fixed effects dynamic panel data models. The importance of the data generating process of the explanatory variables and the proper treatment of initial values for either approach to get asymptotically unbiased estimators are demonstrated both analytically and through Monte Carlo studies.