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Stochastic Frontier Production Function With Errors-In-Variables

Dhawan, Rajeev and Jochumzen, Peter LU (1999) In Working Papers, Department of Economics, Lund University
Abstract
This paper develops a procedure for estimating parameters of a cross-sectional stochastic frontier production function when the factors of production suffer from measurement errors. Specifically, we use Fuller's (1987) reliability ratio concept to develop an estimator for the model in Aigner et al (1977). Our Monte-Carlo simulation exercise illustrates the direction and the severity of bias in the estimates of the elasticity parameters and the returns to scale feature of the production function when using the traditional maximum-likelihood estimator (MLE) in presence of measurement errors. In contrast the reliability ratio based estimator consistently estimates these parameters even under extreme degree of measurement errors. Additionally,... (More)
This paper develops a procedure for estimating parameters of a cross-sectional stochastic frontier production function when the factors of production suffer from measurement errors. Specifically, we use Fuller's (1987) reliability ratio concept to develop an estimator for the model in Aigner et al (1977). Our Monte-Carlo simulation exercise illustrates the direction and the severity of bias in the estimates of the elasticity parameters and the returns to scale feature of the production function when using the traditional maximum-likelihood estimator (MLE) in presence of measurement errors. In contrast the reliability ratio based estimator consistently estimates these parameters even under extreme degree of measurement errors. Additionally, estimates of firm level technical efficiency are severely biased for traditional MLE compared to reliability ratio estimator, rendering inter-firm efficiency comparisons infeasible. The seriousness of measurement errors in a practical setting is demonstrated by using data for a cross-section of publicly traded U.S. corporations. (Less)
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author
organization
publishing date
type
Working Paper
publication status
published
subject
keywords
Errors-In-Variables, Stochastic Frontier, Technica
in
Working Papers, Department of Economics, Lund University
issue
7
publisher
Department of Economics, Lund Universtiy
language
English
LU publication?
yes
id
9839ac35-ca6c-43ac-b627-35cf679dd0c8 (old id 1387581)
alternative location
http://swopec.hhs.se/lunewp/abs/lunewp1999_007.htm
date added to LUP
2009-04-20 12:27:26
date last changed
2016-04-16 09:35:06
@misc{9839ac35-ca6c-43ac-b627-35cf679dd0c8,
  abstract     = {This paper develops a procedure for estimating parameters of a cross-sectional stochastic frontier production function when the factors of production suffer from measurement errors. Specifically, we use Fuller's (1987) reliability ratio concept to develop an estimator for the model in Aigner et al (1977). Our Monte-Carlo simulation exercise illustrates the direction and the severity of bias in the estimates of the elasticity parameters and the returns to scale feature of the production function when using the traditional maximum-likelihood estimator (MLE) in presence of measurement errors. In contrast the reliability ratio based estimator consistently estimates these parameters even under extreme degree of measurement errors. Additionally, estimates of firm level technical efficiency are severely biased for traditional MLE compared to reliability ratio estimator, rendering inter-firm efficiency comparisons infeasible. The seriousness of measurement errors in a practical setting is demonstrated by using data for a cross-section of publicly traded U.S. corporations.},
  author       = {Dhawan, Rajeev and Jochumzen, Peter},
  keyword      = {Errors-In-Variables,Stochastic Frontier,Technica},
  language     = {eng},
  note         = {Working Paper},
  number       = {7},
  publisher    = {Department of Economics, Lund Universtiy},
  series       = {Working Papers, Department of Economics, Lund University},
  title        = {Stochastic Frontier Production Function With Errors-In-Variables},
  year         = {1999},
}