Multivariate spatial autoregressive model for large scale social networks

Xuening Zhu, Danyang Huang, Rui Pan and Hansheng Wang

Journal of Econometrics, 2020, vol. 215, issue 2, 591-606

Abstract: The rapid growth of social network platforms generates a large amount of social network data, where multivariate responses are frequently collected from users. To statistically model such type of data, the multivariate spatial autoregressive (MSAR) model is studied. To estimate the model, the quasi maximum likelihood estimator (QMLE) is obtained under certain technical conditions. However, it is found that the computational cost of QMLE is expensive. To fix this problem, a least squares estimator (LSE) is developed. The corresponding identification conditions and asymptotic properties are investigated. To gauge the finite sample performance of various estimators, a number of simulation studies are conducted. Lastly, a Sina Weibo dataset is analyzed for illustration purpose.

Keywords: Least squares estimator; Quasi maximum likelihood estimator; Multivariate responses; Social network; Network autoregression (search for similar items in EconPapers)
JEL-codes: C51 C55 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:215:y:2020:i:2:p:591-606

DOI: 10.1016/j.jeconom.2018.11.018

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