摘要翻译：
不可分离的面板模型在各种经济环境中都很重要，包括离散选择。本文给出了“时间是随机分配的”或“时间是仪器”等时间齐次条件下不可分模型的辨识和估计结果。给出了离散回归模型在静态和动态条件下，在全非参数和半参数模型中，具有时间效应的平均效应和分位数效应的部分辨识结果。证明了通常的线性固定效应估计量不是辨识平均效应的相合估计量，并给出了相合估计量。给出了识别分位点处理效应的一个简单估计器，解决了从面板数据估计分位点处理效应的重要问题。给出了静态模型和动态模型中整体效应的界限。动态界为在存在未观察到的异质性的情况下估计状态依赖的影响这一重要问题提供了一个部分的辨识解决方案。$T$的影响，即时间周期的数目，通过推导出随着$T$的增长而确定的集合的收缩率来显示。我们还考虑了半参数离散选择模型，发现半参数面板界可能比非参数界紧得多。给出了计算简便的半参数模型方法。我们提出了一种新的推理方法，适用于面板数据和其他设置，并证明了它在大样本下产生一致有效的置信区域。我们给出了经验说明。
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英文标题：
《Average and Quantile Effects in Nonseparable Panel Models》
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作者：
Victor Chernozhukov, Ivan Fernandez-Val, Jinyong Hahn, Whitney Newey
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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：Mathematics        数学
二级分类：Statistics Theory        统计理论
分类描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计：例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
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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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一级分类：Statistics        统计学
二级分类：Statistics Theory        统计理论
分类描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近，贝叶斯推论，决策理论，估计，基础，推论，检验。
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英文摘要：
  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.
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PDF链接：
https://arxiv.org/pdf/0904.1990