论文标题
不可分割的面板模型的低级别近似值
Low-Rank Approximations of Nonseparable Panel Models
论文作者
论文摘要
我们根据低级别因子结构近似值提供了不可分割的面板模型的估计方法。因子结构是通过矩阵完成方法估算的，以应对存在缺失数据的主成分分析的计算挑战。我们表明，所得估计器在大面板中是一致的，但遭受了近似和收缩偏见的影响。我们使用匹配和差分差异方法纠正这些偏差。数字示例和经验应用对选举日注册对美国选民投票率的影响的影响说明了我们方法的特性和实用性。
We provide estimation methods for nonseparable panel models based on low-rank factor structure approximations. The factor structures are estimated by matrix-completion methods to deal with the computational challenges of principal component analysis in the presence of missing data. We show that the resulting estimators are consistent in large panels, but suffer from approximation and shrinkage biases. We correct these biases using matching and difference-in-differences approaches. Numerical examples and an empirical application to the effect of election day registration on voter turnout in the U.S. illustrate the properties and usefulness of our methods.
