Cited by 251Efficiency Analysis Using DEASpatial and Panel Data AnalysisEconomic Growth and Productivity

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Endogeneity in stochastic frontier models

Amsler, Christine, Prokhorov, Artem, Schmidt, Peter·2016·Journal of Econometrics

DOI 10.1016/j.jeconom.2015.06.013

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Authors: Amsler, C., Prokhorov, A., & Schmidt, P.
Affiliations: Michigan State University (US)
The University of Sydney (AU)
Corresponding author: Schmidt, Peter
ORCID: Prokhorov, Artem
Schmidt, Peter
Venue: Journal of Econometrics
Publisher: Elsevier BV
Published: 2016-02
Volume: 190
Issue: 2
Pages: 280-288
ISSN: 0304-4076 (ISSN-L)
DOI: 10.1016/j.jeconom.2015.06.013
OpenAlex: W1492311908
Cited by: 251
References: 34
Citations by year: 2026: 18 · 2025: 22 · 2024: 32 · 2023: 25 · 2022: 29 · 2021: 23 · 2020: 39 · 2019: 20 · 2018: 20 · 2017: 17 · 2016: 5
FWCI: 17.90
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Journal of Econometrics (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
The Sydney eScholarship Repository (The University of Sydney) (submittedVersion)
Topics: Social Sciences › Decision Sciences › Management Science and Operations Research › Efficiency Analysis Using DEA
Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Economic Growth and Productivity
Keywords: stochastic frontier model, endogeneity, systems of equations, production frontier, two-stage least squares, control functions, limited information maximum likelihood, estimator consistency

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