Network double autoregression
Abstract
Modeling high-dimensional time series with simple structures is a challenging problem. This paper proposes a network double autoregression (NDAR) model, which combines the advantages of network structure and the double autoregression (DAR) model, to handle high-dimensional, conditionally heteroscedastic, and network-structured data within a simple framework. The parameters of the model are estimated using quasi-maximum likelihood estimation, and the asymptotic properties of the estimators are derived. The selection of the model's lag order will be based on the Bayesian information criterion. Finite-sample simulations show that the proposed model performs well even with moderate time dimensions and network sizes. Finally, the model is applied to analyze three different categories of stock data.
Cite
@article{arxiv.2412.19251,
title = {Network double autoregression},
author = {Tingting Li and Hao Wang},
journal= {arXiv preprint arXiv:2412.19251},
year = {2024}
}