摘要翻译：
反事实分配是实证经济学中政策分析和分解分析的重要组成部分。在本文中，我们开发了基于回归方法的反事实分布的建模和推理工具。我们所考虑的反事实情景包括与感兴趣结果相关的协变量的分布或给定协变量的结果的条件分布中的ceteris paribus变化。对于这两种情形中的任何一种，我们都导出了联合泛函中心极限定理和基于回归的现状和反事实结果分布估计量的自举有效性结果。这些结果允许我们为反事实变化的函数值效应构造同时置信集，包括对结果的整个分布和分位数函数以及对相关函数的影响。这些置信度集可用于检验功能假说，如无效应、正效应或随机优势。我们的理论适用于一般的反事实变化，并涵盖了主要的回归方法，包括经典回归、分位数回归、持续时间回归和分布回归。我们用美国的数据对工资分解的实证应用来说明结果。作为发展主要结果的一部分，我们介绍了分布回归作为一个全面和灵活的工具来建模和估计\textit{整体}条件分布。我们证明了分布回归包含了Cox持续时间回归，并代表了分位数回归的一个有用的替代方案。我们建立了经验分布回归过程和各种相关函数的泛函中心极限定理和bootstrap有效性结果。
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英文标题：
《Inference on Counterfactual Distributions》
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作者：
Victor Chernozhukov, Ivan Fernandez-Val, Blaise Melly
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最新提交年份：
2013
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分类信息：

一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
  Counterfactual distributions are important ingredients for policy analysis and decomposition analysis in empirical economics. In this article we develop modeling and inference tools for counterfactual distributions based on regression methods. The counterfactual scenarios that we consider consist of ceteris paribus changes in either the distribution of covariates related to the outcome of interest or the conditional distribution of the outcome given covariates. For either of these scenarios we derive joint functional central limit theorems and bootstrap validity results for regression-based estimators of the status quo and counterfactual outcome distributions. These results allow us to construct simultaneous confidence sets for function-valued effects of the counterfactual changes, including the effects on the entire distribution and quantile functions of the outcome as well as on related functionals. These confidence sets can be used to test functional hypotheses such as no-effect, positive effect, or stochastic dominance. Our theory applies to general counterfactual changes and covers the main regression methods including classical, quantile, duration, and distribution regressions. We illustrate the results with an empirical application to wage decompositions using data for the United States.   As a part of developing the main results, we introduce distribution regression as a comprehensive and flexible tool for modeling and estimating the \textit{entire} conditional distribution. We show that distribution regression encompasses the Cox duration regression and represents a useful alternative to quantile regression. We establish functional central limit theorems and bootstrap validity results for the empirical distribution regression process and various related functionals.
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PDF链接：
https://arxiv.org/pdf/0904.0951