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Econometric Information Recovery in Behavioral Networks

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  • George Judge

    (Graduate School and Giannini Foundation, 207 Giannini Hall, University of California Berkeley, Berkeley, CA 94720, USA)

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.

Suggested Citation

  • George Judge, 2016. "Econometric Information Recovery in Behavioral Networks," Econometrics, MDPI, vol. 4(3), pages 1-11, September.
    • Handle: RePEc:gam:jecnmx:v:4:y:2016:i:3:p:38-:d:78167
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    References listed on IDEAS

    as
    1. Lawrence E. Blume & William A. Brock & Steven N. Durlauf & Rajshri Jayaraman, 2015. "Linear Social Interactions Models," Journal of Political Economy, University of Chicago Press, vol. 123(2), pages 444-496.
    2. Bryan S. Graham, 2015. "Methods of Identification in Social Networks," Annual Review of Economics, Annual Reviews, vol. 7(1), pages 465-485, August.
    3. Áureo de Paula, 2015. "Econometrics of network models," CeMMAP working papers 52/15, Institute for Fiscal Studies.
    4. Mittelhammer, Ron C. & Judge, George, 2011. "A family of empirical likelihood functions and estimators for the binary response model," Journal of Econometrics, Elsevier, vol. 164(2), pages 207-217, October.
    5. Charles F. Manski, 1993. "Identification of Endogenous Social Effects: The Reflection Problem," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(3), pages 531-542.
    6. Tiziano Squartini & Enrico Ser-Giacomi & Diego Garlaschelli & George Judge, 2015. "Information Recovery in Behavioral Networks," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-11, May.
    7. Judge,George G. & Mittelhammer,Ron C., 2012. "An Information Theoretic Approach to Econometrics," Cambridge Books, Cambridge University Press, number 9780521869591.
    8. Douglas J. Miller & George Judge, 2015. "Information Recovery in a Dynamic Statistical Markov Model," Econometrics, MDPI, vol. 3(2), pages 1-12, March.
    9. Leonardo Bargigli & Andrea Lionetto & Stefano Viaggiu, 2013. "A Statistical Equilibrium Representation of Markets as Complex Networks," Working Papers - Economics wp2013_23.rdf, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    10. Judge,George G. & Mittelhammer,Ron C., 2012. "An Information Theoretic Approach to Econometrics," Cambridge Books, Cambridge University Press, number 9780521689731.
    11. Cho, Wendy K. Tam & Judge, George G., 2007. "Information theoretic solutions for correlated bivariate processes," Economics Letters, Elsevier, vol. 97(3), pages 201-207, December.
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    Cited by:

    1. George Judge, 2018. "Micro-Macro Connected Stochastic Dynamic Economic Behavior Systems," Econometrics, MDPI, vol. 6(4), pages 1-14, December.

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