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Replication Data for: A Bayesian Multifactor Spatio-Temporal Model for Estimating Time-Varying Network Interdependence

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Replication Data for: A Bayesian Multifactor Spatio-Temporal Model for Estimating Time-Varying Network Interdependence
 
Identifier https://doi.org/10.7910/DVN/B5RVWB
 
Creator Liu, Licheng
Pang, Xun
 
Publisher Harvard Dataverse
 
Description This paper proposes a Bayesian multilevel spatio-temporal model with a time-varying spatial autoregressive coefficient to estimate temporally heterogeneous network interdependence. To tackle the classic reflection problem, we use multiple factors to control for confounding caused by latent homophily and common exposures. We develop a Markov Chain Monte Carlo algorithm to estimate parameters and adopt Bayesian shrinkage to determine the number of factors. Tests on simulated and empirical data show that the proposed model improves identification of network interdependence and is robust to misspecifcation. Our method is applicable to various types of networks and provides a simpler and more flexible alternative to coevolution models.
 
Subject Social Sciences
BAYESIAN STATISTICS
Network analysis
 
Contributor Liu, Licheng