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A stochastic frontier model with correction for sample selection

Heckman's (Ann Econ Soc Meas 4(5), 475-492, 1976; Econometrica 47, 153-161, 1979) sample selection model has been employed in three decades of applications of linear regression studies. This paper builds on this framework to obtain a sample selection correction for the stochastic frontier model. We... Full description

Journal Title: Journal of productivity analysis 2010-08-01, Vol.34 (1), p.15-24
Main Author: Greene, William
Format: Electronic Article Electronic Article
Language: English
Subjects:
Publisher: Boston: Spring Science+Business Media
ID: ISSN: 0895-562X
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title: A stochastic frontier model with correction for sample selection
format: Article
creator:
  • Greene, William
subjects:
  • Accounting/Auditing
  • Article
  • Econometrics
  • Economic models
  • Economic theory
  • Economics
  • Economics and Finance
  • Efficiency
  • Estimation methods
  • Estimators
  • Generalized method of moments
  • Health economics
  • Learning models (Stochastic processes)
  • Linear regression
  • Maximum likelihood estimation
  • Maximum likelihood method
  • Microeconomics
  • Modeling
  • Operations Research/Decision Theory
  • Production efficiency
  • Public health
  • Random variables
  • Regression analysis
  • Sampling methods
  • Selection methods (Regression analysis)
  • Simulation
  • Simulations
  • Software
  • Stochastic models
  • Studies
ispartof: Journal of productivity analysis, 2010-08-01, Vol.34 (1), p.15-24
description: Heckman's (Ann Econ Soc Meas 4(5), 475-492, 1976; Econometrica 47, 153-161, 1979) sample selection model has been employed in three decades of applications of linear regression studies. This paper builds on this framework to obtain a sample selection correction for the stochastic frontier model. We first show a surprisingly simple way to estimate the familiar normal-half normal stochastic frontier model using maximum simulated likelihood. We then extend the technique to a stochastic frontier model with sample selection. In an application that seems superficially obvious, the method is used to revisit the World Health Organization data (WHO in The World Health Report, WHO, Geneva 2000; Tandon et al. in Measuring the overall health system performance for 191 countries, World Health Organization, 2000) where the sample partitioning is based on OECD membership. The original study pooled all 191 countries. The OECD members appear to be discretely different from the rest of the sample. We examine the difference in a sample selection framework.
language: eng
source:
identifier: ISSN: 0895-562X
fulltext: no_fulltext
issn:
  • 0895-562X
  • 1573-0441
url: Link


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descriptionHeckman's (Ann Econ Soc Meas 4(5), 475-492, 1976; Econometrica 47, 153-161, 1979) sample selection model has been employed in three decades of applications of linear regression studies. This paper builds on this framework to obtain a sample selection correction for the stochastic frontier model. We first show a surprisingly simple way to estimate the familiar normal-half normal stochastic frontier model using maximum simulated likelihood. We then extend the technique to a stochastic frontier model with sample selection. In an application that seems superficially obvious, the method is used to revisit the World Health Organization data (WHO in The World Health Report, WHO, Geneva 2000; Tandon et al. in Measuring the overall health system performance for 191 countries, World Health Organization, 2000) where the sample partitioning is based on OECD membership. The original study pooled all 191 countries. The OECD members appear to be discretely different from the rest of the sample. We examine the difference in a sample selection framework.
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subjectAccounting/Auditing ; Article ; Econometrics ; Economic models ; Economic theory ; Economics ; Economics and Finance ; Efficiency ; Estimation methods ; Estimators ; Generalized method of moments ; Health economics ; Learning models (Stochastic processes) ; Linear regression ; Maximum likelihood estimation ; Maximum likelihood method ; Microeconomics ; Modeling ; Operations Research/Decision Theory ; Production efficiency ; Public health ; Random variables ; Regression analysis ; Sampling methods ; Selection methods (Regression analysis) ; Simulation ; Simulations ; Software ; Stochastic models ; Studies
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abstractHeckman's (Ann Econ Soc Meas 4(5), 475-492, 1976; Econometrica 47, 153-161, 1979) sample selection model has been employed in three decades of applications of linear regression studies. This paper builds on this framework to obtain a sample selection correction for the stochastic frontier model. We first show a surprisingly simple way to estimate the familiar normal-half normal stochastic frontier model using maximum simulated likelihood. We then extend the technique to a stochastic frontier model with sample selection. In an application that seems superficially obvious, the method is used to revisit the World Health Organization data (WHO in The World Health Report, WHO, Geneva 2000; Tandon et al. in Measuring the overall health system performance for 191 countries, World Health Organization, 2000) where the sample partitioning is based on OECD membership. The original study pooled all 191 countries. The OECD members appear to be discretely different from the rest of the sample. We examine the difference in a sample selection framework.
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