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|
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