Rights statement: This is the author’s version of a work that was accepted for publication in Journal of Econometrics. 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 Journal of Econometrics, 204, 2, 2018 DOI: 10.1016/j.jeconom.2017.12.009
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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Statistical inference in efficient production with bad inputs and outputs using latent prices and optimal directions
AU - Atkinson, Scott E.
AU - Primont, Daniel
AU - Tsionas, Mike G.
N1 - This is the author’s version of a work that was accepted for publication in Journal of Econometrics. 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 Journal of Econometrics, 204, 2, 2018 DOI: 10.1016/j.jeconom.2017.12.009
PY - 2018/6/2
Y1 - 2018/6/2
N2 - Researchers employ the directional distance function (DDF) to estimate multiple-input and multiple-output production, firm inefficiency, and productivity growth. We relax restrictive assumptions by computing optimal directions subject to profit maximization and cost minimization, correct for the potential endogeneity of inputs and outputs, estimate latent prices for bad outputs, measure firms’ responses to shadow prices rather than actual prices, and introduce an unobserved productivity term into the DDF. For an unbalanced panel of U.S. electric utilities, a model assuming profit-maximization outperforms one assuming cost-minimization, while lagged productivity and energy price have the greatest effect on productivity.
AB - Researchers employ the directional distance function (DDF) to estimate multiple-input and multiple-output production, firm inefficiency, and productivity growth. We relax restrictive assumptions by computing optimal directions subject to profit maximization and cost minimization, correct for the potential endogeneity of inputs and outputs, estimate latent prices for bad outputs, measure firms’ responses to shadow prices rather than actual prices, and introduce an unobserved productivity term into the DDF. For an unbalanced panel of U.S. electric utilities, a model assuming profit-maximization outperforms one assuming cost-minimization, while lagged productivity and energy price have the greatest effect on productivity.
KW - Bayesian
KW - Directional distance
KW - Productivity
KW - Bad outputs
KW - Latent prices
KW - Efficiency
KW - Optimal directions
KW - Shadow prices
U2 - 10.1016/j.jeconom.2017.12.009
DO - 10.1016/j.jeconom.2017.12.009
M3 - Journal article
VL - 204
SP - 131
EP - 146
JO - Journal of Econometrics
JF - Journal of Econometrics
SN - 0304-4076
IS - 2
ER -