据JoE官网显示，来自澳门大学的陈佳、澳门大学的李德贵、英国约克大学的Yu-Ning Li、剑桥大学的Oliver Linton，合作撰写的论文“Estimating time-varying networks for high-dimensional time series”，在国际计量经济学顶级期刊《Journal of Econometrics》线上正式发表。
Title: Estimating time-varying networks for high-dimensional time series
估计高维时间序列的时间变化网络
陈佳
澳门大学
李德贵
澳门大学
Yu-Ning Li
约克大学
Oliver Linton
剑桥大学
We explore time-varying networks for high-dimensional locally stationary time series, using the large VAR model framework with both the transition and (error) precision matrices evolving smoothly over time. Two types of time-varying graphs are investigated: one containing directed edges of Granger causality linkages, and the other containing undirected edges of partial correlation linkages. Under the sparse structural assumption, we propose a penalised local linear method with time-varying weighted group LASSO to jointly estimate the transition matrices and identify their significant entries, and a time-varying CLIME method to estimate the precision matrices. The estimated transition and precision matrices are then used to determine the time-varying network structures. Under some mild conditions, we derive the theoretical properties of the proposed estimates including the consistency and oracle properties. In addition, we extend the methodology and theory to cover highly-correlated large-scale time series, for which the sparsity assumption becomes invalid and we allow for common factors before estimating the factor-adjusted time-varying networks. We provide extensive simulation studies and an empirical application to a large U.S. macroeconomic dataset to illustrate the finite-sample performance of our methods.
本文探索高维局部平稳时间序列的时间变化网络，采用具有平滑随时间演变的转移矩阵和（误差）精度矩阵的大VAR模型框架。研究了两种类型的时间变化图：一种包含格兰杰因果关系的有向边，另一种包含偏相关关系的无向边。在稀疏结构假设下，本文提出了一种带时间变化加权组LASSO惩罚的局部线性方法，用于联合估计转移矩阵并识别其显著条目，以及一种时间变化的CLIME方法来估计精度矩阵。然后利用估计的转移矩阵和精度矩阵来确定时间变化的网络结构。在一些温和条件下，本文推导出了所提出估计量的理论性质，包括一致性和神谕性质。此外，本文还将方法和理论扩展到高度相关的大型时间序列，对于这些序列，稀疏性假设不再有效，本文在估计因子调整的时间变化网络之前允许存在共同因子。本文提供了广泛的模拟研究和对美国大型宏观经济数据集的实证应用，以说明我们方法的有限样本性能。
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