Composite likelihood inference for hidden Markov models for dynamic networks

Francesco Bartolucci, Maria Francesca Marino and Silvia Pandolfi

MPRA Paper from University Library of Munich, Germany

Abstract: We introduce a hidden Markov model for dynamic network data where directed relations among a set of units are observed at different time occasions. The model can also be used with minor adjustments to deal with undirected networks. In the directional case, dyads referred to each pair of units are explicitly modelled conditional on the latent states of both units. Given the complexity of the model, we propose a composite likelihood method for making inference on its parameters. This method is studied in detail for the directional case by a simulation study in which different scenarios are considered. The proposed approach is illustrated by an example based on the well-known Enron dataset about email exchange.

Keywords: Dyads; EM algorithm; Enron dataset; Latent Markov models (search for similar items in EconPapers)
JEL-codes: C13 C14 C18 C3 (search for similar items in EconPapers)
Date: 2015-10-14
New Economics Papers: this item is included in nep-dcm, nep-ecm, nep-net and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:67242

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