Arbeitspapier
Stochastic Frontier Production Function With Errors-In-Variables
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.
- Language
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Englisch
- Bibliographic citation
-
Series: Working Paper ; No. 1999:7
- Classification
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Wirtschaft
Statistical Simulation Methods: General
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
- Subject
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Errors-In-Variables
Stochastic Frontier
Technical Efficiency
Reliability Ratio
- Event
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Geistige Schöpfung
- (who)
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Dhawan, Rajeev
Jochumzen, Peter
- Event
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Veröffentlichung
- (who)
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Lund University, School of Economics and Management, Department of Economics
- (where)
-
Lund
- (when)
-
1999
- Handle
- Last update
-
10.03.2025, 11:41 AM CET
Data provider
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Object type
- Arbeitspapier
Associated
- Dhawan, Rajeev
- Jochumzen, Peter
- Lund University, School of Economics and Management, Department of Economics
Time of origin
- 1999
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