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摘要翻译：
本文基于惩罚最小二乘估计，对高维VAR模型中的Granger因果关系进行了LM检验。为了得到一个在lasso变量选择后保持适当大小的检验，我们提出了一个对干扰变量部分外效应的后双选择过程，并建立了它的一致渐近有效性。我们进行了大量的蒙特卡罗模拟，表明我们的测试在不同的数据生成过程中表现良好，即使没有稀疏性。我们应用我们的测试程序来发现波动溢出网络，我们发现证据表明，与标准的低维VARs相比，高维VARs的因果关系变得更加清晰。
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英文标题：
《Granger Causality Testing in High-Dimensional VARs: a
Post-Double-Selection Procedure》
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作者：
Alain Hecq, Luca Margaritella and Stephan Smeekes
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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 develop an LM test for Granger causality in high-dimensional VAR models based on penalized least squares estimations. To obtain a test retaining the appropriate size after the variable selection done by the lasso, we propose a post-double-selection procedure to partial out effects of nuisance variables and establish its uniform asymptotic validity. We conduct an extensive set of Monte-Carlo simulations that show our tests perform well under different data generating processes, even without sparsity. We apply our testing procedure to find networks of volatility spillovers and we find evidence that causal relationships become clearer in high-dimensional compared to standard low-dimensional VARs.
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PDF链接：
https://arxiv.org/pdf/1902.10991
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