AI 中文总结
本文针对含时变个体异质性的不可分离面板数据，提出线性估计方法，可估计结构与因果效应等，还利用食品杂货购买数据完成实证应用。
AI 中文摘要
本文利用面板数据开发了不可分离模型的结构参数与因果参数的线性估计量。这类模型包含不可观测的时变个体异质性，其可能与回归变量相关。估计方法基于条件平均潜在结果的近似，采用带个体特定参数的线性筛规范；感兴趣的效应通过对个体岭回归的偏差校正平均来估计。我们展示该方法可用于估计因果效应、反事实消费者福利及个体应税收入弹性的平均值。研究表明，所提估计量具有经验贝叶斯解释及多项其他有用性质，构建了可适配离散回归变量且规避部分识别问题的大T渐近理论，并利用食品杂货购买数据估计潜在价格上涨的平均等价变化与无谓损失。
英文摘要
This paper develops linear estimators for structural and causal parameters of nonseparable models using panel data. These models incorporate unobserved, time-varying, individual heterogeneity, which may be correlated with the regressors. Estimation is based on an approximation of a conditional average potential outcome by a linear sieve specification with individual-specific parameters. Effects of interest are estimated by a bias corrected average of individual ridge regressions. We demonstrate how this approach can be applied to estimate causal effects, counterfactual consumer welfare, and averages of individual taxable income elasticities. We show that the proposed estimator has an empirical Bayes interpretation and possesses a number of other useful properties. We formulate Large-$T$ asymptotics that can accommodate discrete regressors and which bypass partial identification in this case. We employ the methods to estimate average equivalent variation and deadweight loss for potential price increases using data on grocery purchases.
