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    Rights statement: This is the author’s version of a work that was accepted for publication in European Economic Review. 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 Economic Review, 141, 2022 DOI: 10.1016/j.euroecorev.2021.103952

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Estimation of large dimensional time varying VARs using copulas

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Estimation of large dimensional time varying VARs using copulas. / Tsionas, Mike G.; Izzeldin, Marwan; Trapani, Lorenzo.
In: European Economic Review, Vol. 141, 103952, 31.01.2022.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Tsionas MG, Izzeldin M, Trapani L. Estimation of large dimensional time varying VARs using copulas. European Economic Review. 2022 Jan 31;141:103952. Epub 2021 Nov 6. doi: 10.1016/j.euroecorev.2021.103952

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Tsionas, Mike G. ; Izzeldin, Marwan ; Trapani, Lorenzo. / Estimation of large dimensional time varying VARs using copulas. In: European Economic Review. 2022 ; Vol. 141.

Bibtex

@article{8c6076f5710443738b16dcd4e483197d,
title = "Estimation of large dimensional time varying VARs using copulas",
abstract = "This paper provides a simple, yet reliable, alternative to the (Bayesian) estimation of large multivariate VARs with time variation in the conditional mean equations and/or in the covariance structure. The original multivariate, n-dimensional model is treated as a set of n univariate estimation problems, and cross-dependence is handled through the use of a copula. This makes it possible to run the estimation of each univariate equation in parallel. Thus, only univariate distribution functions are needed when estimating the individual equations, which are often available in closed form, and easy to handle with MCMC (or other techniques). Thereafter, the individual posteriors are combined with the copula, so obtaining a joint posterior which can be easily resampled. We illustrate our approach using various examples of large time-varying parameter VARs with 129 and even 215 macroeconomic variables.",
keywords = "Vector AutoRegression, Time-varying parameters, Heteroskedasticity, Copulas",
author = "Tsionas, {Mike G.} and Marwan Izzeldin and Lorenzo Trapani",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in European Economic Review. 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 Economic Review, 141, 2022 DOI: 10.1016/j.euroecorev.2021.103952",
year = "2022",
month = jan,
day = "31",
doi = "10.1016/j.euroecorev.2021.103952",
language = "English",
volume = "141",
journal = "European Economic Review",
issn = "0014-2921",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Estimation of large dimensional time varying VARs using copulas

AU - Tsionas, Mike G.

AU - Izzeldin, Marwan

AU - Trapani, Lorenzo

N1 - This is the author’s version of a work that was accepted for publication in European Economic Review. 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 Economic Review, 141, 2022 DOI: 10.1016/j.euroecorev.2021.103952

PY - 2022/1/31

Y1 - 2022/1/31

N2 - This paper provides a simple, yet reliable, alternative to the (Bayesian) estimation of large multivariate VARs with time variation in the conditional mean equations and/or in the covariance structure. The original multivariate, n-dimensional model is treated as a set of n univariate estimation problems, and cross-dependence is handled through the use of a copula. This makes it possible to run the estimation of each univariate equation in parallel. Thus, only univariate distribution functions are needed when estimating the individual equations, which are often available in closed form, and easy to handle with MCMC (or other techniques). Thereafter, the individual posteriors are combined with the copula, so obtaining a joint posterior which can be easily resampled. We illustrate our approach using various examples of large time-varying parameter VARs with 129 and even 215 macroeconomic variables.

AB - This paper provides a simple, yet reliable, alternative to the (Bayesian) estimation of large multivariate VARs with time variation in the conditional mean equations and/or in the covariance structure. The original multivariate, n-dimensional model is treated as a set of n univariate estimation problems, and cross-dependence is handled through the use of a copula. This makes it possible to run the estimation of each univariate equation in parallel. Thus, only univariate distribution functions are needed when estimating the individual equations, which are often available in closed form, and easy to handle with MCMC (or other techniques). Thereafter, the individual posteriors are combined with the copula, so obtaining a joint posterior which can be easily resampled. We illustrate our approach using various examples of large time-varying parameter VARs with 129 and even 215 macroeconomic variables.

KW - Vector AutoRegression

KW - Time-varying parameters

KW - Heteroskedasticity

KW - Copulas

U2 - 10.1016/j.euroecorev.2021.103952

DO - 10.1016/j.euroecorev.2021.103952

M3 - Journal article

VL - 141

JO - European Economic Review

JF - European Economic Review

SN - 0014-2921

M1 - 103952

ER -