Identifiability of the Stochastic Frontier Models
AbstractThis 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.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 8032.
Date of creation: Jun 2007
Date of revision: Jan 2008
Identification; Stochastic frontier model; Information Matrix; Signal to Noise Ratio;
Find related papers by JEL classification:
- C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-05-05 (All new papers)
- NEP-ECM-2008-05-05 (Econometrics)
- NEP-EFF-2008-05-05 (Efficiency & Productivity)
- NEP-ORE-2008-05-05 (Operations Research)
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