Econometric information recovery in behavioral networks - George Judge (Econometrics, 2016)
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: C1; C10; C24; 330; random exponential networks; binary and weighed networks; inverse problem; adjacency matrix; Cressie-Read family of divergence measures; conditional moment conditions; self organized behavior systems