Estimation of dynamic panel data models with a lot of heterogeneity
研究了当个体效应方差与误差方差之比趋于无穷时，常用系统GMM估计量不一致的问题，提出了在T>3时仍一致的新两步系统估计量，并重新检验了金融发展与经济增长的关系。
The commonly used 1-step and 2-step System GMM estimators for the panel AR(1) model are inconsistent under mean stationarity when the ratio of the variance of the individual effects to the variance of the idiosyncratic errors is unbounded when N→∞. The reason for their inconsistency is that their weight matrices select moment conditions that do not identify the autoregressive parameter. This paper proposes a new 2-step System estimator that is still consistent in this case provided that T>3. Unlike the commonly used 2-step System estimator, the new estimator uses an estimator of the optimal weight matrix that remains consistent in this case. We also show that the commonly used 1-step and 2-step Arellano-Bond GMM estimators and the Random Effects Quasi MLE remain consistent under the same conditions. To illustrate the usefulness of our new System estimator we revisit the growth study of Levine et al. (2000 Levine, R., Loayza, N., Beck, T. (2000). Financial intermediation and growth: causality and causes. Journal of Monetary Economics 46(1):31–77. doi:https://doi.org/10.1016/S0304-3932(00)00017-9[Crossref], [Web of Science ®] , [Google Scholar]).