Accounting for sample overlap in economics meta‐analyses: The generalized‐weights method in practice
展示了广义权重方法在经济学元分析中处理样本重叠的实际应用，解决了数据聚合、估计方法和效应量指标等差异带来的挑战，并通过蒙特卡洛模拟证明该方法能提高效率、控制假阳性率。
Abstract Meta‐analyses in economics frequently exhibit considerable overlap among primary samples. If not addressed, sample overlap leads to efficiency losses and inflated rates of false positives at the meta‐analytical level. In previous work, we proposed a generalized‐weights (GW) approach to handle sample overlap. This approach effectively approximates the correlation structure between primary estimates using information on sample sizes and overlap degrees in the primary studies. This paper demonstrates the application of the GW method to economics meta‐analyses, addressing practical challenges that are likely to be encountered. We account for variations in data aggregation levels, estimation methods, and effect size metrics, among other issues. We derive explicit covariance formulas for different scenarios, evaluate the accuracy of the approximations, and employ Monte Carlo simulations to demonstrate how the method enhances efficiency and restores the false positive rate to its nominal level.