JIVE FOR PANEL DYNAMIC SIMULTANEOUS EQUATIONS MODELS
研究了面板动态联立方程模型中结构方程的参数估计，发现Alvarez和Arellano型GMM估计量在T³/N趋于非零常数时有渐近偏误，提出刀切法降低偏误并推导了极限分布，蒙特卡洛模拟验证了无偏估计对统计推断的重要性。
We consider the method of moments estimation of a structural equation in a panel dynamic simultaneous equations model under different sample size combinations of cross-sectional dimension, N , and time series dimension, T . Two types of linear transformation to remove the individual-specific effects from the model, first difference and forward orthogonal demeaning, are considered. We show that the Alvarez and Arellano (2003) type GMM estimator under both transformations is consistent only if ${T \over N} \to 0$ as $\left( {N,T} \right) \to \infty $ . However, it is asymptotically biased if ${{{T^3}} \over N} \to \kappa \ne 0 < \infty$ as $\left( {N,T} \right) \to \infty $ . Since the validity of statistical inference depends critically on whether an estimator is asymptotically unbiased, we suggest a jackknife bias reduction method and derive its limiting distribution. Monte Carlo studies are conducted to demonstrate the importance of using an asymptotically unbiased estimator to obtain valid statistical inference.