论文标题
在非线性面板模型中识别随时间变化的反事实参数
Identification of time-varying counterfactual parameters in nonlinear panel models
论文作者
论文摘要
我们开发了一个通用框架，用于在一类具有固定效果和时间效果的非线性半参数图模型中识别反事实参数。我们的方法适用于具有离散或连续回归器的离散结果模型（例如，双向固定效果二进制选择）或连续结果（例如，审查回归）。我们的结果不需要关于结果方程的错误项或时间均匀性的参数假设。我们的主要结果集中在静态模型上，其中一组结果适用于没有任何外种条件的模型。我们表明，在这类模型中，发现反事实的生存分布（点或部分）。该参数是针对应用实践中大多数部分和边际影响的基础，基于Blundell和Powell（2003，2004）所定义的平均结构函数。据我们所知，我们的第一个结果是二进制选择的平均部分和边缘效应，以及有固定效果和非逻辑错误的有序选择模型。
We develop a general framework for the identification of counterfactual parameters in a class of nonlinear semiparametric panel models with fixed effects and time effects. Our method applies to models for discrete outcomes (e.g., two-way fixed effects binary choice) or continuous outcomes (e.g., censored regression), with discrete or continuous regressors. Our results do not require parametric assumptions on the error terms or time-homogeneity on the outcome equation. Our main results focus on static models, with a set of results applying to models without any exogeneity conditions. We show that the survival distribution of counterfactual outcomes is identified (point or partial) in this class of models. This parameter is a building block for most partial and marginal effects of interest in applied practice that are based on the average structural function as defined by Blundell and Powell (2003, 2004). To the best of our knowledge, ours are the first results on average partial and marginal effects for binary choice and ordered choice models with two-way fixed effects and non-logistic errors.
