Image representing the file: CWP0223-Identifying-network-ties-from-panel-data-theory-and-an-applicat
Coherent Identifier 20.500.12592/8j1wnb

Image representing the file: CWP0223-Identifying-network-ties-from-panel-data-theory-and-an-applicat

18 January 2023


By identifying the social interactions matrix W0, our results allow the recovery of aggregate network characteristics such as the degree distribution and patterns of homophily, as well as node-level statistics such as the strength of social interactions between nodes, and the centrality of nodes. [...] The favourable performance of the elastic net in these cases also relates to the literature on the ‘effective number of parameters’ (or ‘effective degrees of freedom’) in the estimation of sparse models (Tibshirani and Taylor, 2012). [...] Since the pattern of displacement is unobserved – and, in fact, insurgents have incentives to obfuscate their strategy – the current method is applied to fully recover the network and bound the effects of the end of the military occupation on conflict.11 Our paper proceeds as follows. [...] The sixth assumption pertains to relation between the nature of repeated multiple observations of the outcome and covariates and restrictions on the stability of W. [...] In unweighted networks, the diagonal of the square of the social interactions matrix captures the number of reciprocated links for each individual or, in the case of undirected networks, the popularity of those individuals.

Published in
United Kingdom