Econometric Models of Network Data

Dear Colleagues,

Statistical inference when data are grouped into clusters is an important issue in empirical work, and failure to control for within-cluster correlation can lead to potential large biases and misleadingly small standard errors. A particular data structure related to cluster effects is that of networks. This has attracted considerable attention with regard to spillover effects in education, production, financial markets, trade, and many others.

Within a given network, observations are not independent, and the dependence structure is related to the network position of the observation and its connections. There is no obvious pattern to construct clusters or groups in this case. Network models differ from standard cluster and panel data structures in the heterogeneity of the groups, which needs to be defined ad hoc within the network as there is no obvious way to group observations. They also differ from spatial econometric models that require a pre-specification of the adjacency matrix and a distance measure within the network topology.

This Special Issue aims to publish theoretical and empirical contributions where the main contribution is on network data structures within econometric models. Specific topics of interest are dyadic data, centrality measures, graph theory, peer effects, clusters, multivariate panel data techniques, agents-based models, and other related topics.

Prof. Dr. Gabriel Montes-Rojas
Prof. Dr. Walter Sosa-Escudero
Guest Editors

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• network data
• dyadic data
• panel data
• clusters
• centrality measures
• graph theory