• Corpus ID: 239024781

A General Modeling Framework for Network Autoregressive Processes

  title={A General Modeling Framework for Network Autoregressive Processes},
  author={Hang Yin and Abolfazl Safikhani and George Michailidis},
The paper develops a general flexible framework for Network Autoregressive Processes (NAR), wherein the response of each node linearly depends on its past values, a prespecified linear combination of neighboring nodes and a set of node-specific covariates. The corresponding coefficients are node-specific, while the framework can accommodate heavier than Gaussian errors with both spatialautorgressive and factor based covariance structures. We provide a sufficient condition that ensures the… 


Network Vector Autoregression
We consider here a large-scale social network with a continuous response observed for each node at equally spaced time points. The responses from different nodes constitute an ultra-high dimensional
Community Network Auto-Regression for High-Dimensional Time Series
Modeling responses on the nodes of a large-scale network is an important task that arises commonly in practice. This paper proposes a community network vector autoregressive (CNAR) model, which
Grouped Network Vector Autoregression
In the study of time series analysis, it is of great interest to model a continuous response for all the individuals at equally spaced time points. With the rapid advance of social network sites,
Generalized Network Autoregressive Processes and the GNAR Package
The GNAR package is introduced, which fits, predicts, and simulates from a powerful new class of generalized network autoregressive processes, which consist of a multivariate time series along with a real, or inferred, network that provides information about inter-variable relationships.
Many theoretical results for lasso require the samples to be iid. Recent work has provided guarantees for lasso assuming that the time series is generated by a sparse Vector Auto-Regressive (VAR)
New Introduction to Multiple Time Series Analysis
This reference work and graduate level textbook considers a wide range of models and methods for analyzing and forecasting multiple time series. The models covered include vector autoregressive,
Asymptotic Distributions of Quasi-Maximum Likelihood Estimators for Spatial Autoregressive Models
This paper investigates asymptotic properties of the maximum likelihood estimator and the quasi-maximum likelihood estimator for the spatial autoregressive model. The rates of convergence of those
Large Dimensional Factor Analysis
Econometric analysis of large dimensional factor models has been a heavily researched topic in recent years. This review surveys the main theoretical results that relate to static factor models or