Home > Research > Publications & Outputs > Bayesian input–output table update using a benc...

Research

Associated organisational unit

Electronic data

  • paper-2019.12.16-MLL-

    Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in Economic Systems Research on 09/01/2020, available online: https://www.tandfonline.com/doi/abs/10.1080/09535314.2019.1707170

    Accepted author manuscript, 345 KB, PDF document

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

Links

Text available via DOI:

Keywords

View graph of relations

Bayesian input–output table update using a benchmark LASSO prior

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

Published

Standard

Bayesian input–output table update using a benchmark LASSO prior. / Tsionas, Mike G.
In: Economic Systems Research, Vol. 32, No. 3, 02.07.2020, p. 413-427.

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

Harvard

Tsionas, MG 2020, 'Bayesian input–output table update using a benchmark LASSO prior', Economic Systems Research, vol. 32, no. 3, pp. 413-427. https://doi.org/10.1080/09535314.2019.1707170

APA

Tsionas, M. G. (2020). Bayesian input–output table update using a benchmark LASSO prior. Economic Systems Research, 32(3), 413-427. https://doi.org/10.1080/09535314.2019.1707170

Vancouver

Tsionas MG. Bayesian input–output table update using a benchmark LASSO prior. Economic Systems Research. 2020 Jul 2;32(3):413-427. Epub 2020 Jan 9. doi: 10.1080/09535314.2019.1707170

Author

Tsionas, Mike G. / Bayesian input–output table update using a benchmark LASSO prior. In: Economic Systems Research. 2020 ; Vol. 32, No. 3. pp. 413-427.

Bibtex

@article{fbd30fed2b6b4efa837f6ae28150ddaa,
title = "Bayesian input–output table update using a benchmark LASSO prior",
abstract = "We propose updating a multiplier matrix subject to final demand and total output constraints, where the prior multiplier matrix is weighted against a LASSO prior. We update elements of the Leontief inverse, from which we can derive posterior densities of the entries in input-output tables. As the parameter estimates required by far exceed the available observations, many zero entries deliver a sparse tabulation. We address that problem with a new statistical model wherein we adopt a LASSO prior. We develop novel numerical techniques and perform a detailed Monte Carlo study to examine the performance of the new approach under different configurations of the input-output table. The new techniques are applied to a 196 ×196 U.S. input-output table for 2012. ",
keywords = "Input–output tables, Bayesian inference, Markov Chain Monte Carlo, LASSO priors",
author = "Tsionas, {Mike G.}",
note = "This is an Accepted Manuscript of an article published by Taylor & Francis in Economic Systems Research on 09/01/2020, available online: https://www.tandfonline.com/doi/abs/10.1080/09535314.2019.1707170",
year = "2020",
month = jul,
day = "2",
doi = "10.1080/09535314.2019.1707170",
language = "English",
volume = "32",
pages = "413--427",
journal = "Economic Systems Research",
number = "3",

}

RIS

TY - JOUR

T1 - Bayesian input–output table update using a benchmark LASSO prior

AU - Tsionas, Mike G.

N1 - This is an Accepted Manuscript of an article published by Taylor & Francis in Economic Systems Research on 09/01/2020, available online: https://www.tandfonline.com/doi/abs/10.1080/09535314.2019.1707170

PY - 2020/7/2

Y1 - 2020/7/2

N2 - We propose updating a multiplier matrix subject to final demand and total output constraints, where the prior multiplier matrix is weighted against a LASSO prior. We update elements of the Leontief inverse, from which we can derive posterior densities of the entries in input-output tables. As the parameter estimates required by far exceed the available observations, many zero entries deliver a sparse tabulation. We address that problem with a new statistical model wherein we adopt a LASSO prior. We develop novel numerical techniques and perform a detailed Monte Carlo study to examine the performance of the new approach under different configurations of the input-output table. The new techniques are applied to a 196 ×196 U.S. input-output table for 2012.

AB - We propose updating a multiplier matrix subject to final demand and total output constraints, where the prior multiplier matrix is weighted against a LASSO prior. We update elements of the Leontief inverse, from which we can derive posterior densities of the entries in input-output tables. As the parameter estimates required by far exceed the available observations, many zero entries deliver a sparse tabulation. We address that problem with a new statistical model wherein we adopt a LASSO prior. We develop novel numerical techniques and perform a detailed Monte Carlo study to examine the performance of the new approach under different configurations of the input-output table. The new techniques are applied to a 196 ×196 U.S. input-output table for 2012.

KW - Input–output tables

KW - Bayesian inference

KW - Markov Chain Monte Carlo

KW - LASSO priors

U2 - 10.1080/09535314.2019.1707170

DO - 10.1080/09535314.2019.1707170

M3 - Journal article

VL - 32

SP - 413

EP - 427

JO - Economic Systems Research

JF - Economic Systems Research

IS - 3

ER -