Bootstrap inference for network vector autoregression in large-scale social network
- Authors
- Hong, Manho; Hwang, Eunju
- Issue Date
- Dec-2021
- Publisher
- SPRINGER HEIDELBERG
- Keywords
- Network vector autoregression; Stationary bootstrap; Residual bootstrap; Prediction intervals
- Citation
- JOURNAL OF THE KOREAN STATISTICAL SOCIETY, v.50, no.4, pp.1238 - 1258
- Journal Title
- JOURNAL OF THE KOREAN STATISTICAL SOCIETY
- Volume
- 50
- Number
- 4
- Start Page
- 1238
- End Page
- 1258
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/82843
- DOI
- 10.1007/s42952-021-00115-7
- ISSN
- 1226-3192
- Abstract
- A large amount of online social network data such as Facebook or Twitter are extensively generated by the growth of social network platforms in recent years. Development of a network time series model and its statistical inference are as important as the rapid progress on the social network technology and evolution. In this work we consider a network vector autoregression for the large-scale social network, proposed by Zhu et al. (Ann Stat 45(3):1096-1123, 2017), and study its bootstrap estimation and bootstrap forecast. In order to suggest a bootstrap version of parameter estimates in the underlying model, two bootstrap methods are combined together: stationary bootstrap and classical residual bootstrap. Consistency of the bootstrap estimator is established and the bootstrap confidence intervals are constructed. Moreover, we obtain bootstrap prediction intervals for multi-step ahead future values. A Monte-Carlo study illustrates better finite-sample performances of our bootstrap technique than those by the standard method.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - 사회과학대학 > 응용통계학과 > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/82843)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.