Estimating Long and Short Run Effects in Static Panel Models
评估了当数据生成过程为动态误差成分模型时，使用静态面板模型估计短期和长期参数带来的四种估计量的偏差，并分析了偏差的决定因素。
Abstract This paper assesses the biases of four different estimators with respect to the short run and the long run parameters if a static panel model is used, although the data generating process is a dynamic error components model. We analytically derive the associated biases and provide a discussion of the determinants thereof. Our analytical and numerical results as well as Monte Carlo simulations illustrate that the asymptotic bias of both the within and the between parameter with respect to the short run and long run impact can be substantial, depending on the memory of the data generating process, the length of the time series and the importance of the cross-sectional variation in the explanatory variables.