摘要翻译：
我们研究了在进入两步估计过程的第一步估计中包含许多协变量的含义。我们发现，当\textit{included}协变量的数量相对于样本大小的平方根“大”时，会出现一阶偏差，使得标准的推理过程无效。我们证明了jackknife能够一致地估计这种“多协变量”偏差，从而提供了一种新的自动偏差校正的两步点估计器。折刀还一致地估计原始两步点估计器的标准误差。为了推论，我们发展了一个有效的后偏差校正bootstrap近似，该近似解释了由折刀偏差校正引入的额外变异性。我们发现Jacknife偏差校正点估计器和bootstrap偏差校正后推断器在仿真中表现出色，与传统的两步点估计器和推断过程相比，它们在包含许多协变量时不具有鲁棒性，提供了重要的改进。我们将我们的结果应用于一系列不同的治疗效果、政策评估和其他应用微观经济学环境。特别地，我们详细讨论了生产函数和边际处理效果估计。
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英文标题：
《Two-Step Estimation and Inference with Possibly Many Included Covariates》
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作者：
Matias D. Cattaneo, Michael Jansson, Xinwei Ma
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最新提交年份：
2018
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分类信息：

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：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：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：
  We study the implications of including many covariates in a first-step estimate entering a two-step estimation procedure. We find that a first order bias emerges when the number of \textit{included} covariates is "large" relative to the square-root of sample size, rendering standard inference procedures invalid. We show that the jackknife is able to estimate this "many covariates" bias consistently, thereby delivering a new automatic bias-corrected two-step point estimator. The jackknife also consistently estimates the standard error of the original two-step point estimator. For inference, we develop a valid post-bias-correction bootstrap approximation that accounts for the additional variability introduced by the jackknife bias-correction. We find that the jackknife bias-corrected point estimator and the bootstrap post-bias-correction inference perform excellent in simulations, offering important improvements over conventional two-step point estimators and inference procedures, which are not robust to including many covariates. We apply our results to an array of distinct treatment effect, policy evaluation, and other applied microeconomics settings. In particular, we discuss production function and marginal treatment effect estimation in detail.
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PDF链接：
https://arxiv.org/pdf/1807.10100