Estimation of large dimensional time varying VARs using copulas

Tsionas, Mike G. and Izzeldin, Marwan and Trapani, Lorenzo (2022) Estimation of large dimensional time varying VARs using copulas. European Economic Review, 141: 103952. ISSN 0014-2921

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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.

Item Type:
Journal Article
Journal or Publication Title:
European Economic Review
Additional Information:
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
Uncontrolled Keywords:
?? vector autoregressiontime-varying parametersheteroskedasticitycopulasfinanceeconomics and econometrics ??
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Deposited On:
09 Dec 2021 09:19
Last Modified:
17 Feb 2024 01:17