NETS: Network estimation for time series
提出NETS算法，将大量时间序列建模为稀疏向量自回归，同时估计预测性格兰杰关系网络和同期偏相关网络，应用于90只蓝筹股波动率分析，提升预测效果。
Summary We model a large panel of time series as a vector autoregression where the autoregressive matrices and the inverse covariance matrix of the system innovations are assumed to be sparse. The system has a network representation in terms of a directed graph representing predictive Granger relations and an undirected graph representing contemporaneous partial correlations. A LASSO algorithm called NETS is introduced to estimate the model. We apply the methodology to analyze a panel of volatility measures of 90 blue chips. The model captures an important fraction of total variability, on top of what is explained by volatility factors, and improves out‐of‐sample forecasting.