Average and Quantile Effects in Nonseparable Panel Models
非可分面板模型中的平均效应与分位数效应AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确研究面板数据中的平均与分位数处理效应及其部分识别，并讨论动态状态依赖，直接对应面板、分布效应与因果推断。
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非可分面板模型中的平均效应与分位数效应
- 研究问题
- 在时间同质性条件下，非可分面板模型能识别哪些平均与分位数效应，如何区分状态依赖和不可观测异质性，并据此估计和推断？
- 方法与思路
- 作者在静态与动态、非参数与半参数框架中推导离散解释变量效应的部分识别界限，构造估计方法，并研究期数增加时识别集的收缩。
- 材料陈述的贡献
- 摘要称通常的线性固定效应估计量对已识别平均效应不一致，并给出一致替代估计、分位数效应估计、动态界限及均匀渐近有效的置信区域。
- 阅读关注点
- 阅读全文时应核对时间同质性的形式、已识别效应与总体效应的区别、状态依赖界限，以及期数增长和半参数限制缩紧界限所需的条件。
- 阅读边界
- 摘要未列明时间同质性假设、实证样本或效应数值；固定效应不一致与置信区域有效性的结论有模型边界，不能脱离具体条件概括。
原文摘要
Nonseparable panel models are important in a variety of economic settings, including discrete choice. This paper gives identification and estimation results for nonseparable models under time-homogeneity conditions that are like “time is randomly assigned” or “time is an instrument.” Partial-identification results for average and quantile effects are given for discrete regressors, under static or dynamic conditions, in fully nonparametric and in semiparametric models, with time effects. It is shown that the usual, linear, fixed-effects estimator is not a consistent estimator of the identified average effect, and a consistent estimator is given. A simple estimator of identified quantile treatment effects is given, providing a solution to the important problem of estimating quantile treatment effects from panel data. Bounds for overall effects in static and dynamic models are given. The dynamic bounds provide a partial-identification solution to the important problem of estimating the effect of state dependence in the presence of unobserved heterogeneity. The impact of T, the number of time periods, is shown by deriving shrinkage rates for the identified set as T grows. We also consider semiparametric, discrete-choice models and find that semiparametric panel bounds can be much tighter than nonparametric bounds. Computationally convenient methods for semiparametric models are presented. We propose a novel inference method that applies in panel data and other settings and show that it produces uniformly valid confidence regions in large samples. We give empirical illustrations.