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    Rights statement: This is the author’s version of a work that was accepted for publication in Economics Letters. 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 Economics Letters, 165, 2018 DOI: 10.1016/j.econlet.2018.02.005

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Bayesian local influence analysis: With an application to stochastic frontiers

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

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Bayesian local influence analysis: With an application to stochastic frontiers. / Tsionas, Mike G.
In: Economics Letters, Vol. 165, 04.2018, p. 54-57.

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

Harvard

Tsionas, MG 2018, 'Bayesian local influence analysis: With an application to stochastic frontiers', Economics Letters, vol. 165, pp. 54-57. https://doi.org/10.1016/j.econlet.2018.02.005

APA

Tsionas, M. G. (2018). Bayesian local influence analysis: With an application to stochastic frontiers. Economics Letters, 165, 54-57. https://doi.org/10.1016/j.econlet.2018.02.005

Vancouver

Tsionas MG. Bayesian local influence analysis: With an application to stochastic frontiers. Economics Letters. 2018 Apr;165:54-57. Epub 2018 Feb 6. doi: 10.1016/j.econlet.2018.02.005

Author

Tsionas, Mike G. / Bayesian local influence analysis : With an application to stochastic frontiers. In: Economics Letters. 2018 ; Vol. 165. pp. 54-57.

Bibtex

@article{6c82eccb69fa409c8f5cc6d88ce3c0d5,
title = "Bayesian local influence analysis: With an application to stochastic frontiers",
abstract = "A Bayesian alternative to Zhuo (2018) is presented. The method is of general interest as it presents an explicit formula for the local sensitivity of log marginal likelihood when observations vary by a small amount. The remarkable feature is that the formula is very easy to compute and does not require knowledge of the marginal likelihood which is, invariably, extremely difficult to compute. Similar expressions are derived for posterior moments and other functions of interest, including inefficiency. Methods for examining prior sensitivity in a straightforward way are also presented. The methods are illustrated in the context of a stochastic production frontier.",
keywords = "Local influence, Bayesian analysis, Marginal likelihood, Posterior moments, Markov Chain Monte Carlo",
author = "Tsionas, {Mike G.}",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in Economics Letters. 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 Economics Letters, 165, 2018 DOI: 10.1016/j.econlet.2018.02.005",
year = "2018",
month = apr,
doi = "10.1016/j.econlet.2018.02.005",
language = "English",
volume = "165",
pages = "54--57",
journal = "Economics Letters",
issn = "0165-1765",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Bayesian local influence analysis

T2 - With an application to stochastic frontiers

AU - Tsionas, Mike G.

N1 - This is the author’s version of a work that was accepted for publication in Economics Letters. 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 Economics Letters, 165, 2018 DOI: 10.1016/j.econlet.2018.02.005

PY - 2018/4

Y1 - 2018/4

N2 - A Bayesian alternative to Zhuo (2018) is presented. The method is of general interest as it presents an explicit formula for the local sensitivity of log marginal likelihood when observations vary by a small amount. The remarkable feature is that the formula is very easy to compute and does not require knowledge of the marginal likelihood which is, invariably, extremely difficult to compute. Similar expressions are derived for posterior moments and other functions of interest, including inefficiency. Methods for examining prior sensitivity in a straightforward way are also presented. The methods are illustrated in the context of a stochastic production frontier.

AB - A Bayesian alternative to Zhuo (2018) is presented. The method is of general interest as it presents an explicit formula for the local sensitivity of log marginal likelihood when observations vary by a small amount. The remarkable feature is that the formula is very easy to compute and does not require knowledge of the marginal likelihood which is, invariably, extremely difficult to compute. Similar expressions are derived for posterior moments and other functions of interest, including inefficiency. Methods for examining prior sensitivity in a straightforward way are also presented. The methods are illustrated in the context of a stochastic production frontier.

KW - Local influence

KW - Bayesian analysis

KW - Marginal likelihood

KW - Posterior moments

KW - Markov Chain Monte Carlo

U2 - 10.1016/j.econlet.2018.02.005

DO - 10.1016/j.econlet.2018.02.005

M3 - Journal article

VL - 165

SP - 54

EP - 57

JO - Economics Letters

JF - Economics Letters

SN - 0165-1765

ER -