论文标题
网络向量自回归模型的同时估计和组识别具有异质节点
Simultaneous Estimation and Group Identification for Network Vector Autoregressive Model with Heterogeneous Nodes
论文作者
论文摘要
大型社会或金融网络中的个人或公司通常出于各种原因表现出相当异质的行为。在这项工作中，我们提出了一个具有潜在组结构的网络矢量自回归模型，以模拟从网络节点观察到的异质动态模式，以自然合并群体网络效应和时间变量的固定效应。在我们的框架中，可以通过最小化最小二乘类型的目标函数来同时估算模型参数和网络节点成员身份。特别是，我们的理论研究允许在达到模型参数和组成员身份的估计一致性时，g的数量被过度指定，从而显着提高了所提出的方法的鲁棒性。当正确指定G时，可以根据估计器的渐近正态性来为模型参数做出有效的统计推断。开发了一个数据驱动的标准，以始终如一地识别实际使用的真实组号。广泛的仿真研究和两个真实的数据示例用于证明所提出的方法的有效性。
Individuals or companies in a large social or financial network often display rather heterogeneous behaviors for various reasons. In this work, we propose a network vector autoregressive model with a latent group structure to model heterogeneous dynamic patterns observed from network nodes, for which group-wise network effects and timeinvariant fixed-effects can be naturally incorporated. In our framework, the model parameters and network node memberships can be simultaneously estimated by minimizing a least-squares type objective function. In particular, our theoretical investigation allows the number of latent groups G to be over-specified when achieving the estimation consistency of the model parameters and group memberships, which significantly improves the robustness of the proposed approach. When G is correctly specified, valid statistical inference can be made for model parameters based on the asymptotic normality of the estimators. A data-driven criterion is developed to consistently identify the true group number for practical use. Extensive simulation studies and two real data examples are used to demonstrate the effectiveness of the proposed methodology.
