B1127
Title: Network autoregressive processes and their applications
Authors: George Michailidis - University of California, Los Angeles (United States) [presenting]
Abstract: Models are developed 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 spatial-autorgressive and factor-based covariance structures. We consider the stability (stationarity) of the underlying NAR and develop estimators for both a fixed, as well as a diverging number of network nodes. We also address the issue of selecting the network connectivity and the impact of misspecifying it on inference. The framework is illustrated on both synthetic and real data sets.
