Network autoregression model with grouped factor structures

ZHANG Zhiyuan; ZHU Xuening

doi:10.13471/j.cnki.acta.snus.2023A027

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Network autoregression model with grouped factor structures

Column | 更新时间：2023-11-07

- Network autoregression model with grouped factor structures
- Acta Scientiarum Naturalium Universitatis Sunyatseni Vol. 62, Issue 5, Pages: 24-37(2023)
- 作者机构：
  
  School of Data Science, Fudan University, Shanghai 200433,China
- 作者简介：
  
  ZHANG Zhiyuan（zhiyuanzhang20@fudan.edu.cn）
  ZHU Xuening（xueningzhu@fudan.edu.cn）
- 基金信息：
- DOI：10.13471/j.cnki.acta.snus.2023A027
  CLC： F22
- Published：25 September 2023，
  
  Published Online：30 August 2023，
  
  Received：04 April 2023，
  
  Accepted：11 April 2023
扫描看全文
.· 前沿聚焦：优化技术与应用专栏 ·[J].中山大学学报(自然科学版),2023,62(05):24-37.

ZHANG Zhiyuan,ZHU Xuening.Network autoregression model with grouped factor structures[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2023,62(05):24-37.
.· 前沿聚焦：优化技术与应用专栏 ·[J].中山大学学报(自然科学版),2023,62(05):24-37. DOI： 10.13471/j.cnki.acta.snus.2023A027.

ZHANG Zhiyuan,ZHU Xuening.Network autoregression model with grouped factor structures[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2023,62(05):24-37. DOI： 10.13471/j.cnki.acta.snus.2023A027.

摘要

Abstract

Network autoregression and factor model are effective methods for modeling network time series data. In this study

we propose a network autoregression model with a factor structure that incorporates a latent group structure to address nodal heterogeneity within the network. An iterative algorithm is employed to minimize a least-squares objective function

allowing for simultaneous estimation of both the parameters and the group structure. To determine the unknown number of groups and factors

a PIC criterion is introduced. Additionally

statistical inference of the estimated parameters is presented. To assess the validity of the proposed estimation and inference procedures

we conduct extensive numerical studies. We also demonstrate the utility of our model using a stock dataset obtained from the Chinese A-Share stock market.

关键词

Keywords

network autoregressionfactor structureheterogeneitylatent group structurenetwork time series

references

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ZHANG Zhiyuan

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