Rights statement: This is the author’s version of a work that was accepted for publication in European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Operational Research, 258, 3, 2017 DOI: 10.1016/j.ejor.2016.09.033
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Microfoundations for stochastic frontiers
AU - Tsionas, Efthymios
N1 - This is the author’s version of a work that was accepted for publication in European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Operational Research, 258, 3, 2017 DOI: 10.1016/j.ejor.2016.09.033
PY - 2017/5/1
Y1 - 2017/5/1
N2 - The purpose of the paper is to propose microfoundations for stochastic frontier models. Previous work shows that a simple Bayesian learning model supports gamma distributions for technical inefficiency in stochastic frontier models. The conclusion depends on how the problem is formulated and what assumptions are made about the sampling process and the prior. After the new formulation of the problem it turns out that the distribution of the one-sided error component does not belong to a known family. Moreover, we find that without specifying a utility function or even the cost inefficiency function, the relative effectiveness of managerial input can be determined using only cost data and estimates of the returns to scale. The point of this construction is that features of the inefficiency function u(z) can be recovered from the data, based on the solid microfoundation of expected utility of profit maximization but the model does not make a prediction about the distribution.
AB - The purpose of the paper is to propose microfoundations for stochastic frontier models. Previous work shows that a simple Bayesian learning model supports gamma distributions for technical inefficiency in stochastic frontier models. The conclusion depends on how the problem is formulated and what assumptions are made about the sampling process and the prior. After the new formulation of the problem it turns out that the distribution of the one-sided error component does not belong to a known family. Moreover, we find that without specifying a utility function or even the cost inefficiency function, the relative effectiveness of managerial input can be determined using only cost data and estimates of the returns to scale. The point of this construction is that features of the inefficiency function u(z) can be recovered from the data, based on the solid microfoundation of expected utility of profit maximization but the model does not make a prediction about the distribution.
KW - Economics
KW - Stochastic frontier analysis
KW - Microfoundations
KW - Bayesian learning
KW - Learning-by-Doing
U2 - 10.1016/j.ejor.2016.09.033
DO - 10.1016/j.ejor.2016.09.033
M3 - Journal article
VL - 258
SP - 1165
EP - 1170
JO - European Journal of Operational Research
JF - European Journal of Operational Research
SN - 0377-2217
IS - 3
ER -