摘要翻译：
我们考虑了一个高维回归方程组中的估计和推断，考虑了协变量和误差过程中的时间和截面相关性，涵盖了弱时间相关性的相当一般的形式。采用LASSO（最小绝对收缩和选择算子）的多个回归序列进行变量选择，并通过块乘法器引导程序仔细选择总惩罚水平，以考虑方程的多重性和数据中的相关性。相应地，将派生具有共同选择的调优参数的oracle属性。我们进一步对系统的多个目标参数提供了高质量的去偏同时推理。我们提供了测试过程的bootstrap一致性结果，这些结果基于具有相关数据的$z$-估计量的一般Bahadur表示。仿真结果表明，所提出的推理过程具有良好的性能。最后，我们运用该方法量化了金融市场中文本情绪指数的溢出效应，并检验了行业间的关联性。
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英文标题：
《LASSO-Driven Inference in Time and Space》
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作者：
Victor Chernozhukov, Wolfgang K. H\"ardle, Chen Huang, Weining Wang
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最新提交年份：
2020
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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：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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英文摘要：
  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 temporal dependence. A sequence of regressions with many regressors using LASSO (Least Absolute Shrinkage and Selection Operator) is applied for variable selection purpose, 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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PDF链接：
https://arxiv.org/pdf/1806.05081