The findings are shown as following. First, the coefficient value of network autocorrelation term 0. In other words, these social connections are not randomly distributed across space but tend to form spatial clusters where neighboring links are more similar. Second, controlling spatial autocorrelation in the data, a distance decay effect on the formation of online social links is unveiled with a much smaller scaling exponent of the distances i.
This research provides a useful modeling framework to analyze the real-world driving forces that characterize the patterns of social interactions in virtual space and thus advance our understanding in the connection of virtual and real spaces. A spatial econometric modeling of online social interactions using microblogs.
Informatics New Jersey Institute of Technology. Fingerprint econometrics.
Dynamic discrete choice models capture various notions of dynamic effects, state dependence, heterogeneity, and spurious correlation in a panel data setting. This project generalizes existing models to incorporate possible contemporaneous and intertemporal spatial interaction effects.
Spatial dynamics in discrete choices are of special interest.
Special attention is paid to the specification and estimation of such panel data models. Even though the development of econometric methods is the main focus of this project, empirical studies with panel data from developing countries illustrate the useful of the new models and the feasibility of the methodologies.
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