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摘要翻译：
在本研究中，我们在秩不变性和秩平稳性假设下发展了一种新的分位数处理效应(QTE)估计方法。Ishihara(2020)在这些假设下探索了不可分面板数据模型的辨识，并提出了基于最小距离法的参数估计。然而，当协变量的维数较大时，用该方法进行最小距离估计计算量较大。为了克服这一问题，我们提出了一种基于分位数回归和最小距离法的两步估计方法。然后我们证明了估计量的一致渐近性质和非参数自举的有效性。Monte Carlo研究表明，我们的估计方法在有限样本下具有良好的性能。最后，我们给出了两个实证例子，来估计保险提供对家庭生产和电视观看对儿童认知发展的分布影响。
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英文标题：
《Panel Data Quantile Regression for Treatment Effect Models》
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作者：
Takuya Ishihara
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最新提交年份：
2021
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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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英文摘要：
In this study, we develop a novel estimation method for quantile treatment effects (QTE) under rank invariance and rank stationarity assumptions. Ishihara (2020) explores identification of the nonseparable panel data model under these assumptions and proposes a parametric estimation based on the minimum distance method. However, when the dimensionality of the covariates is large, the minimum distance estimation using this process is computationally demanding. To overcome this problem, we propose a two-step estimation method based on the quantile regression and minimum distance methods. We then show the uniform asymptotic properties of our estimator and the validity of the nonparametric bootstrap. The Monte Carlo studies indicate that our estimator performs well in finite samples. Finally, we present two empirical illustrations, to estimate the distributional effects of insurance provision on household production and TV watching on child cognitive development.
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PDF链接：
https://arxiv.org/pdf/2001.04324
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