推论
估计员
Lasso(编程语言)
维数之咒
协变量
计算机科学
一致性(知识库)
计量经济学
数学
算法
统计
人工智能
万维网
作者
Victor Chernozhukov,Wolfgang Karl Härdle,Chen Huang,Weining Wang
出处
期刊:Social Science Research Network
[Social Science Electronic Publishing]
日期:2018-01-01
被引量:6
摘要
We consider the estimation and inference in a system of high-dimensional regression equations allowing for temporal and cross-sectional dependency in covariates and error processes, covering rather general forms of weak dependence. A sequence of large-scale regressions with LASSO is applied to reduce the dimensionality, and an overall penalty level is carefully chosen by a block multiplier bootstrap procedure to account for multiplicity of the equations and dependencies in the data. Correspondingly, oracle properties with a jointly selected tuning parameter are derived. We further provide high-quality de-biased simultaneous inference on the many target parameters of the system. We provide bootstrap consistency results of the test procedure, which are based on a general Bahadur representation for the Z-estimators with dependent data. Simulations demonstrate good performance of the proposed inference procedure. Finally, we apply the method to quantify spillover effects of textual sentiment indices in a financial market and to test the connectedness among sectors.
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