摘要
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.
出处
《中山大学学报（自然科学版）（中英文）》
CAS
CSCD
北大核心
2023年第5期24-37,共14页
Acta Scientiarum Naturalium Universitatis Sunyatseni
基金
Supported by National Natural Science Foundation of China(72222009,71991472)。
