Some strange properties of panel data estimators
研究当个体间参数存在差异但估计时未考虑时，面板数据估计量可能出现的严重偏误，并通过蒙特卡洛模拟考察了Anderson-Hsiao估计量在这种设定错误下的表现。
Abstract We study the biases that are likely to arise in practice with panel data when parameters vary across individuals, but this is not allowed for in estimation. We consider both stationary and non‐stationary regressors. We find that biases can be severe for relatively small parameter variation, and that this problem is hard to detect. We study in some detail by Monte‐Carlo the performance of the Anderson‐Hsiao estimator in the presence of this particular mis‐specification.