GMM Estimation with persistent panel data: an application to production functions
研究了使用面板数据估计Cobb-Douglas生产函数时，标准一阶差分GMM估计量存在大样本偏差，通过利用初始条件的平稳性限制可大幅减小偏差，并用美国制造业公司数据验证了扩展GMM估计量得到更合理的参数估计。
This paper considers the estimation of Cobb-Douglas production functions using panel data covering a large sample of companies observed for a small number of time periods. GMM estimatorshave been found to produce large finite-sample biases when using the standard first-differenced estimator. These biases can be dramatically reduced by exploiting reasonable stationarity restrictions on the initial conditions process. Using data for a panel of R&Dperforming US manufacturing companies we find that the additional instruments used in our extended GMM estimator yield much more reasonable parameter estimates.