A Functional Coefficients Network Autoregressive Model
arxiv(2024)
摘要
The paper introduces a flexible model for the analysis of multivariate
nonlinear time series data. The proposed Functional Coefficients Network
Autoregressive (FCNAR) model considers the response of each node in the network
to depend in a nonlinear fashion to each own past values (autoregressive
component), as well as past values of each neighbor (network component). Key
issues of model stability/stationarity, together with model parameter
identifiability, estimation and inference are addressed for error processes
that can be heavier than Gaussian for both fixed and growing number of network
nodes. The performance of the estimators for the FCNAR model is assessed on
synthetic data and the applicability of the model is illustrated on multiple
indicators of air pollution data.
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