Pseudo Panel Data Models With Cohort Interactive Effects
研究了当真实面板数据不可得时，利用重复截面数据构建伪面板的线性估计方法，扩展了标准模型以处理因子残差和队列交互效应，并通过蒙特卡洛模拟和厄瓜多尔劳动供给数据验证了方法的有效性。
When genuine panel data samples are not available, repeated cross-sectional surveys can be used to form so-called pseudo panels. In this article, we investigate the properties of linear pseudo panel data estimators with fixed number of cohorts and time observations. We extend standard linear pseudo panel data setup to models with factor residuals by adapting the quasi-differencing approach developed for genuine panels. In a Monte Carlo study, we find that the proposed procedure has good finite sample properties in situations with endogeneity, cohort interactive effects, and near nonidentification. Finally, as an illustration the proposed method is applied to data from Ecuador to study labor supply elasticity. Supplementary materials for this article are available online.