A score-driven approach to time-varying network


Prometeia organizes training sessions on economic, financial and methodological issues open to whom can be interested (the opportunity to participate is subject to availability of seats).

A score-driven approach to time-varying network

 Prof. Giacomo Bormetti (Università di Bologna)

Where: Training Room, Bologna Headquarters (Piazza Trento e Trieste 3)

When: 13/11/2019; from 14:30 to 16:30

Motivated by the evidence that real-world networks evolve in time and may exhibit non-stationary features, we propose an extension of the Exponential Random Graph Models (ERGMs) accommodating the time variation of network parameters.

Within the ERGM framework, a network realization is sampled from a static probability distribution defined parametrically in terms of network statistics. Inspired by the fast-growing literature on Dynamic Conditional Score-driven models, in our approach, each parameter evolves according to an updating rule driven by the score of the conditional distribution. We demonstrate the flexibility of the score-driven ERGMs both as data generating processes and as filters.

Our method captures dynamical network dependencies, that emerge from the data, and allows for a test discriminating between static or time-varying parameters. Finally, we corroborate our findings with the application to networks from real financial and political systems exhibiting non-stationary dynamics.

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