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利用匹配数据或纵向网络数据估计溢出效应AI 解读

Social interactions often play a key role in determining the impact of policies, but measuring the magnitude of spillover effects empirically is notoriously challenging because, in most applications, a person’s relationships are likely to reflect her own characteristics (homophily), and people who are connected are likely to be affected by the same shocks (common factors). In addition, a significant share of social interactions is likely to occur through variables that are…

相关随机系数面板模型中处理状态不变者的平均处理效应AI 解读

Summary Correlated random coefficient (CRC) models provide a useful framework for estimating average treatment effects (ATE) with panel data by accommodating heterogeneous treatment effects and flexible patterns of selection. In their simplest form, they lead to the well‐known difference‐in‐differences estimator. CRC models yield estimates of ATE for “movers” (i.e., cross‐sectional units whose treatment status changed over time) while ATE for “stayers” (i.e., cross‐sectional…

稀疏匹配数据下双向固定效应线性模型的估计与推断AI 解读

Models with multiway fixed effects are frequently used to address selection on unobservables. The data used for estimating these models often contain few observations per value of either indexing variable (sparsely matched data). I show that this sparsity has important implications for inference and propose an asymptotically valid inference method based on subsetting. Sparsity also has important implications for point estimation when covariates or instrumental variables are…

横截面依赖下动态面板模型的估计：利用簇依赖提高效率AI 解读

Summary This paper considers the estimation of dynamic panel data models when data are suspected to exhibit cross‐sectional dependence. A new estimator is defined that uses cross‐sectional dependence for efficiency while being robust to the misspecification of the form of the cross‐sectional dependence. We show that using cross‐sectional dependence for estimation is important to obtain an estimator that is more efficient than existing estimators. This new estimator also uses…