2021/10/18 by Hang Yin, Yin, Hang, Abolfazl Safikhani +3 · 2 citations
Economics, Econometrics and Finance · Social Sciences · #62H11 #62J07 #62M10 #FOS: Computer and information sciences #FOS: Mathematics #Health disparities and outcomes #Methodology (stat.ME) #Regional Economics and Spatial Analysis #Spatial and Panel Data Analysis #Statistics Theory (math.ST)
paper · pdf · doi:10.48550/arxiv.2110.09596
openalex publication_date 2021/10/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A general flexible framework for Network Autoregressive Processes (NAR) is developed, wherein the response of each node in the network 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, and the framework can accommodate heavier than Gaussian errors with spatial-autoregressive, factor-based, or in certain settings general covariance structures. We provide a sufficient condition that ensures the stability (stationarity) of the underlying NAR that is significantly weaker than its counterparts in previous work in the literature. Further, we develop ordinary and (estimated) generalized least squares estimators for both fixed, as well as diverging numbers of network nodes, and also provide their ridge regularized counterparts that exhibit better performance in large network settings, together with their asymptotic distributions. We derive their asymptotic distributions that can be used for testing various hypotheses of interest to practitioners. We also address the issue of misspecifying the network connectivity and its impact on the aforementioned asymptotic distributions of the various NAR parameter estimators. The framework is illustrated on both synthetic and real air pollution data.