Locally Robust Semiparametric Estimation
Locally Robust Semiparametric Estimation
复制标题
DOI：
10.1920/wp.cem.2016.3116
复制
发表时间：
2016-07
期刊：
影响因子：
6.1
通讯作者：
Hidehiko Ichimura;Whitney Newey;V. Chernozhukov;J. Escanciano;J. Robins
中科院分区：
文献类型：
--
作者：
Hidehiko Ichimura;Whitney Newey;V. Chernozhukov;J. Escanciano;J. Robins
许多经济和因果参数依赖于非参数或高维的第一步。我们给出了一个一般的局部鲁棒/正交矩函数的GMM，第一步没有影响，局部，平均矩函数的建设。使用这些正交矩可以减少模型选择和正则化偏差，这在许多应用中非常重要，特别是对于机器学习的第一步。此外，当矩函数数量与感兴趣的参数相同时，相关的标准误差对错误指定具有鲁棒性。我们使用这些正交矩和交叉拟合来构造高维条件分位数函数和高维状态变量动态离散选择参数函数的去偏机器学习估计。我们表明，额外的第一步所需的正交矩函数没有影响，全球范围内，平均正交矩函数。我们给出了一个一般的方法来估计这些额外的第一步。我们刻画了双鲁棒性，并给出了各种新的双鲁棒矩函数。我们给出了渐近理论的一般和简单的正则性条件。
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 first steps have no effect, locally, on average moment functions. Using these orthogonal moments reduces model selection and regularization bias, as is important in many applications, especially for machine learning first steps. Also, associated standard errors are robust to misspecification when there is the same number of moment functions as parameters of interest. We use these orthogonal moments and cross‐fitting to construct debiased machine learning estimators of functions of high dimensional conditional quantiles and of dynamic discrete choice parameters with high dimensional state variables. We show that additional first steps needed for the orthogonal moment functions have no effect, globally, on average orthogonal moment functions. We give a general approach to estimating those additional first steps. We characterize double robustness and give a variety of new doubly robust moment functions. We give general and simple regularity conditions for asymptotic theory.