NETS: Network Estimation for Time Series
NETS: Network Estimation for Time Series
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DOI：
10.2139/ssrn.2249909
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发表时间：
2018-10
期刊：
影响因子：
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通讯作者：
M. Barigozzi;C. Brownlees
中科院分区：
文献类型：
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作者：
M. Barigozzi;C. Brownlees
这项工作提出了新的多变量时间序列网络分析技术。我们将多变量时间序列的网络定义为一个图，其中顶点表示过程的分量，边表示非零的长期部分相关性。然后，我们介绍了一种两步套索过程，称为NETS，用于估计高维稀疏长期偏相关网络。这种方法基于过程的VAR近似，并允许将长期联系分解为系统的动态依赖关系和同时依赖关系的贡献。分析了估计量的大样本性质，建立了非零长时偏相关一致选择和估计的条件。该方法通过对美国蓝筹股小组的应用进行了说明。
This work proposes novel network analysis techniques for multivariate time series. We define the network of a multivariate time series as a graph where vertices denote the components of the process and edges denote non zero long run partial correlations. We then introduce a two step LASSO procedure, called NETS, to estimate high dimensional sparse Long Run Partial Correlation networks. This approach is based on a VAR approximation of the process and allows to decompose the long run linkages into the contribution of the dynamic and contemporaneous dependence relations of the system. The large sample properties of the estimator are analysed and we establish conditions for consistent selection and estimation of the non zero long run partial correlations. The methodology is illustrated with an application to a panel of U.S. bluechips.