Work

A Functional Coefficients Network Autoregressive Model

Year: 2024

Type: article

Abstract: 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 two data sets; the first on multiple indicators of air pollution data and the second on Covid-19 cases in Florida counties.

Source: Statistica Sinica

DOI: 10.5705/ss.202022.0402

Authors: Hang Yin, Abolfazl Safikhani, George Michailidis

Institutions: University of California, Los Angeles, George Mason University, University of Florida

Language: English

Topic: Spatial and Panel Data Analysis

Subfield: Economics and Econometrics

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: bronze

Funder: National Science Foundation

Award: ATD: Spatio-Temporal Modeling for Identifying Changes in Land Use