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摘要翻译：
许多经济和因果参数依赖于非参数或高维的第一步。给出了GMM的局部鲁棒/正交矩函数的一般构造，其中矩条件对第一步的导数为零。我们证明了正交矩函数可以通过在识别矩上加上第一步对识别矩影响的非参数影响函数来构造。正交矩减少模型选择和正则化偏差，这在许多应用中非常重要，尤其是在机器学习的第一步。我们给出了高维条件分位数泛函的去偏机器学习估计和具有高维状态变量的动态离散选择参数的去偏机器学习估计。我们证明了在识别矩的基础上增加非参数影响函数提供了一个广义的正交矩的构造，包括正则性条件，并证明了非参数影响函数对它所依赖的其他未知函数是鲁棒的。给出了一种估计非参数影响函数中未知函数的一般方法，并将其用于高维条件位置学习器函数的自动去偏估计。我们给出了各种新的双鲁棒矩方程，并刻画了双鲁棒性。给出了一般而简单的正则性条件，并将其应用于高维回归分位数泛函的渐近推论和具有高维状态变量的动态离散选择参数的渐近推论。
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英文标题：
《Locally Robust Semiparametric Estimation》
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作者：
Victor Chernozhukov, Juan Carlos Escanciano, Hidehiko Ichimura,
Whitney K. Newey, James M. Robins
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最新提交年份：
2020
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分类信息：
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：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：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：
Many economic and causal parameters depend on nonparametric or high dimensional first steps. We give a general construction of locally robust/orthogonal moment functions for GMM, where moment conditions have zero derivative with respect to first steps. We show that orthogonal moment functions can be constructed by adding to identifying moments the nonparametric influence function for the effect of the first step on identifying moments. Orthogonal moments reduce model selection and regularization bias, as is very important in many applications, especially for machine learning first steps. We give debiased machine learning estimators of functionals of high dimensional conditional quantiles and of dynamic discrete choice parameters with high dimensional state variables. We show that adding to identifying moments the nonparametric influence function provides a general construction of orthogonal moments, including regularity conditions, and show that the nonparametric influence function is robust to additional unknown functions on which it depends. We give a general approach to estimating the unknown functions in the nonparametric influence function and use it to automatically debias estimators of functionals of high dimensional conditional location learners. We give a variety of new doubly robust moment equations and characterize double robustness. We give general and simple regularity conditions and apply these for asymptotic inference on functionals of high dimensional regression quantiles and dynamic discrete choice parameters with high dimensional state variables.
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PDF链接：
https://arxiv.org/pdf/1608.00033
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