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Identifiability of the Stochastic Frontier Models

Bandyopadhyay, Debdas and Das, Arabinda (2007): Identifiability of the Stochastic Frontier Models.

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Abstract

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.

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