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    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

    Accepted author manuscript, 1.34 MB, PDF document

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

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    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, ??, ?, 2018 DOI: 10.1016/j.jeconom.2017.12.009

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    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

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Statistical inference in efficient production with bad inputs and outputs using latent prices and optimal directions

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Statistical inference in efficient production with bad inputs and outputs using latent prices and optimal directions. / Atkinson, Scott E.; Primont, Daniel; Tsionas, Mike G.
In: Journal of Econometrics, Vol. 204, No. 2, 02.06.2018, p. 131-146.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Atkinson, SE, Primont, D & Tsionas, MG 2018, 'Statistical inference in efficient production with bad inputs and outputs using latent prices and optimal directions', Journal of Econometrics, vol. 204, no. 2, pp. 131-146. https://doi.org/10.1016/j.jeconom.2017.12.009

APA

Atkinson, S. E., Primont, D., & Tsionas, M. G. (2018). Statistical inference in efficient production with bad inputs and outputs using latent prices and optimal directions. Journal of Econometrics, 204(2), 131-146. https://doi.org/10.1016/j.jeconom.2017.12.009

Vancouver

Atkinson SE, Primont D, Tsionas MG. Statistical inference in efficient production with bad inputs and outputs using latent prices and optimal directions. Journal of Econometrics. 2018 Jun 2;204(2):131-146. Epub 2018 Feb 15. doi: 10.1016/j.jeconom.2017.12.009

Author

Atkinson, Scott E. ; Primont, Daniel ; Tsionas, Mike G. / Statistical inference in efficient production with bad inputs and outputs using latent prices and optimal directions. In: Journal of Econometrics. 2018 ; Vol. 204, No. 2. pp. 131-146.

Bibtex

@article{79a12148737d430cbb1cef511d596fad,
title = "Statistical inference in efficient production with bad inputs and outputs using latent prices and optimal directions",
abstract = "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{\textquoteright} 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.",
keywords = "Bayesian, Directional distance, Productivity, Bad outputs, Latent prices, Efficiency, Optimal directions, Shadow prices",
author = "Atkinson, {Scott E.} and Daniel Primont and Tsionas, {Mike G.}",
note = "This is the author{\textquoteright}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",
year = "2018",
month = jun,
day = "2",
doi = "10.1016/j.jeconom.2017.12.009",
language = "English",
volume = "204",
pages = "131--146",
journal = "Journal of Econometrics",
issn = "0304-4076",
publisher = "Elsevier BV",
number = "2",

}

RIS

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 -