Testing Convergence in Income Distribution*
指出广义矩估计（GMM）在检验收入分布收敛性时样本利用效率低，提出更有效的普通最小二乘（OLS）估计量，蒙特卡洛模拟显示OLS更精确且偏差更小。
Abstract The generalized method of moments (GMM) estimator is often used to test for convergence in income distribution in a dynamic panel set‐up. We argue that though consistent, the GMM estimator utilizes the sample observations inefficiently. We propose a simple ordinary least squares (OLS) estimator with more efficient use of sample information. Our Monte Carlo study shows that the GMM estimator can be very imprecise and severely biased in finite samples. In contrast, the OLS estimator overcomes these shortcomings.