Stochastic Frontier Production Function With Errors-In-Variables
(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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1387581
- author
- Dhawan, Rajeev and Jochumzen, Peter LU
- organization
- publishing date
- 1999
- type
- Working paper/Preprint
- 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 University
- 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
- 2016-04-04 11:41:59
- date last changed
- 2018-11-21 21:06:36
@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}}, keywords = {{Errors-In-Variables; Stochastic Frontier; Technica}}, language = {{eng}}, note = {{Working Paper}}, number = {{7}}, publisher = {{Department of Economics, Lund University}}, series = {{Working Papers, Department of Economics, Lund University}}, title = {{Stochastic Frontier Production Function With Errors-In-Variables}}, url = {{https://lup.lub.lu.se/search/files/5834516/2056967}}, year = {{1999}}, }