Network double autoregression
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917879743512576 |
|---|---|
| author | Li, Tingting Wang, Hao |
| author_facet | Li, Tingting Wang, Hao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_19251 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Network double autoregression Li, Tingting Wang, Hao Methodology Statistics Theory 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. |
| title | Network double autoregression |
| topic | Methodology Statistics Theory |
| url | https://arxiv.org/abs/2412.19251 |