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Inference for Nonparametric Productivity Networks: A Pseudo-likelihood Approach

Author

Listed:
  • Moriah B. Bostian

    () (Department of Economics, Lewis & Clark College, Portland, OR USA)

  • Cinzia Daraio

    () (Department of Computer, Control and Management Engineering Antonio Ruberti (DIAG), University of Rome La Sapienza, Rome, Italy)

  • Rolf Fare

    () (Department of Applied Economics, Oregon State University, Corvallis, OR USA)

  • Shawna Grosskopf

    () (Department of Economics, Oregon State University, Corvallis, OR USA)

  • Maria Grazia Izzo

    () (Department of Computer, Control and Management Engineering Antonio Ruberti (DIAG), University of Rome La Sapienza, Rome, Italy ; Center for Life Nano Science, Fondazione Istituto Italiano di Tecnologia (IIT), Rome, Italy)

  • Luca Leuzzi

    () (CNR-NANOTEC, Institute of Nanotechnology, Soft and Living Matter Lab, Rome, Italy ; Department of Physics, Sapienza University of Rome, Italy)

  • Giancarlo Ruocco

    () (Center for Life Nano Science, Fondazione Istituto Italiano di Tecnologia (IIT), Rome, Italy ; Department of Physics, Sapienza University of Rome, Italy)

  • William L. Weber

    () (Department of Economics and Finance, Southeast Missouri State University, Cape Girardeau, MO USA)

Abstract

Networks are general models that represent the relationships within or between systems widely studied in statistical mechanics. Nonparametric productivity networks (Network-DEA) typically analyzes the networks in a descriptive rather than statistical framework. We fill this gap by developing a general framework-involving information science, machine learning and statistical inference from the physics of complex systems- for modeling the production process based on the axiomatics of Network-DEA connected to Georgescu-Roegen funds and flows model. The proposed statistical approach allows us to infer the network topology in a Bayesian framework. An application to assess knowledge productivity at a world-country level is provided.

Suggested Citation

  • Moriah B. Bostian & Cinzia Daraio & Rolf Fare & Shawna Grosskopf & Maria Grazia Izzo & Luca Leuzzi & Giancarlo Ruocco & William L. Weber, 2018. "Inference for Nonparametric Productivity Networks: A Pseudo-likelihood Approach," DIAG Technical Reports 2018-06, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
  • Handle: RePEc:aeg:report:2018-06
    as

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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Network DEA ; Bayesian statistics ; Generalized multicomponent Ising Model ; Georgescu Roegen;

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