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Spatial autoregression with repeated measurements for social networks

Author

Listed:
  • Danyang Huang
  • Xiangyu Chang
  • Hansheng Wang

Abstract

Spatial autoregressive model (SAR) is found useful to estimate the social autocorrelation in social networks recently. However, the rapid development of information technology enables researchers to collect repeated measurements for a given social network. The SAR model for social networks is designed for cross-sectional data and is thus not feasible. In this article, we propose a new model which is referred to as SAR with random effects (SARRE) for social networks. It could be considered as a natural combination of two types of models, the SAR model for social networks and a particular type of mixed model. To solve the problem of high computational complexity in large social networks, a pseudo-maximum likelihood estimate (PMLE) is proposed. The asymptotic properties of the estimate are established. We demonstrate the performance of the proposed method by extensive numerical studies and a real data example.

Suggested Citation

  • Danyang Huang & Xiangyu Chang & Hansheng Wang, 2018. "Spatial autoregression with repeated measurements for social networks," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(15), pages 3715-3727, August.
    • Handle: RePEc:taf:lstaxx:v:47:y:2018:i:15:p:3715-3727
    DOI: 10.1080/03610926.2017.1361989
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