Bandyopadhyay, Debdas and Das, Arabinda (2007): Identifiability of the Stochastic Frontier Models.
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This paper examines the identifiability of the standard single-equation stochastic frontier models with uncorrelated and correlated error components giving, inter alia, mathematical content to the notion of “near-identifiability” of a statistical model. It is seen that these models are at least locally identifiable but suffer from the “near-identifiability” problem. Our results also highlight the pivotal role played by the Signal to Noise Ratio in the “near-identifiablity” of the stochastic frontier models.
|Item Type:||MPRA Paper|
|Original Title:||Identifiability of the Stochastic Frontier Models|
|Keywords:||Identification, Stochastic frontier model, Information Matrix, Signal to Noise Ratio|
|Subjects:||C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C10 - General
C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation, Validation, and Selection
C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models; Multiple Variables > C31 - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
|Depositing User:||Arabinda Das|
|Date Deposited:||30. Apr 2008 05:51|
|Last Modified:||16. Feb 2013 08:11|
Aigner, D., K. Lovell and P. Schmidt (1977). Formulation and Estimation of Stochastic Frontier Production Function Models. Journal of Econometrics, 6, 21-37.
Bandyopadhyay and Das (2006). On measures of Technical Inefficiency and Production Uncertainty in Stochastic Frontier Production Model with Correlated Error Components. Journal of Productivity Analysis, Vol. 26, No. 2, 165-180.
Battese, G. and G. Corra (1977). Estimation of a Production Frontier Model, with Application to the Pastoral Zone off Eastern Australia. Australian Journal of Agricultural Economics, 21, 169-179.
Burns, R. C. J. (2004). The Simulated Maximum Likelihood Estimation of Stochastic Frontier Models with Correlated Error Components. Unpublished dissertation, Department of Econometrics and Business Statistics, The University of Sydney, Australia.
Capitanio, A., A. Azzalini and E. Stanghellini (2003). Graphical Models for Skew-normal Variates. Scandinavian Journal of Statistics, 30, 129-144.
Cramer, H. (1946). Mathematical Methods in Statistics. Princeton University Press, Princeton.
Greene, W. H. (1990). A Gamma Distributed Stochastic Frontier Model. Journal of Econometrics, 1990, 46, 141-164.
Greene, W. H. (1993). The Econometric Approach to Efficiency Analysis, in H. O. Fried, C. A. K. Lovell and S. S. Schmidt, eds., The Measurement of Productive Efficiency: Techniques and Applications. New York: Oxford University Press.
Kumbhakar, S. and C. A. K. Lovell (2000). Stochastic Frontier Analysis. Cambridge University Press, Cambridge.
Meeusen, W. and J. van den Broeck (1977). Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error. International Economic Review, 18, 435-444.
Pal, M. and A. Sengupta (1999). A Model of FPF with Correlated Error Components: An Application to Indian Agriculture. Sankhya, Vol-61, Series B, 337-350.
Rao, B. L. S. Prakash (1992). Identifiability in Stochastic Models, Academic Press, New York.
Ritter, C. and L. Simar (1997). Pitfalls of Normal-Gamma Stochastic Frontier Models, Journal of Productivity Analysis, 8, pp. 167-182.
Rothenberg, T. J. (1971). Identification in Parametric Models. Econometrica, 39, No. 3, 577-591.
Smith, M. D. (2004). Stochastic Frontier Model with Correlated Error Components. Working paper, Department of Econometrics and Business Statistics, The University of Sydney, Australia.
Stevenson, R. (1980). Likelihood Functions for Generalized Stochastic Frontier Estimation. Journal of Econometrics, 13, 57-66.