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Inference for Nonparametric Productivity Networks: A Pseudo-likelihood Approach

Moriah B. Bostian (), Cinzia Daraio (), Rolf Fare (), Shawna Grosskopf (), Maria Grazia Izzo (), Luca Leuzzi (), Giancarlo Ruocco () and William L. Weber ()
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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

No 2018-06, DIAG Technical Reports from Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza"

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.

Keywords: Network DEA; Bayesian statistics; Generalized multicomponent Ising Model; Georgescu Roegen (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-eff
Date: 2018
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