Econometric Information Recovery in Behavioral Networks

George Judge ()

Econometrics, 2016, vol. 4, issue 3, 1-11

Abstract: In this paper, we suggest an approach to recovering behavior-related, preference-choice network information from observational data. We model the process as a self-organized behavior based random exponential network-graph system. To address the unknown nature of the sampling model in recovering behavior related network information, we use the Cressie-Read (CR) family of divergence measures and the corresponding information theoretic entropy basis, for estimation, inference, model evaluation, and prediction. Examples are included to clarify how entropy based information theoretic methods are directly applicable to recovering the behavioral network probabilities in this fundamentally underdetermined ill posed inverse recovery problem.

Keywords: random exponential networks; binary and weighed networks; inverse problem; adjacency matrix; Cressie-Read family of divergence measures; conditional moment conditions; self organized behavior systems (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jecnmx:v:4:y:2016:i:3:p:38-:d:78167

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