Bayesian Estimation of Large Dimensional Time Varying VARs Using Copulas

32 Pages Posted: 17 Jan 2020

See all articles by Mike Tsionas

Mike Tsionas

Lancaster University

Marwan Izzeldin

Lancaster University Management School

Lorenzo Trapani

affiliation not provided to SSRN

Date Written: December 27, 2019

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. With our new methodology, 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. 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). Estimation is carried out in parallel for the individual equations. Thereafter, the individual posteriors are combined with the copula, so obtaining a joint posterior which can be easily resampled. We illustrate our approach by applying it to a large time-varying parameter VAR with 25 macroeconomic variables.

Keywords: Vector AutoRegressive Moving Average models, Time-Varying parameters, Copulas

JEL Classification: C11, C13

Suggested Citation

Tsionas, Efthymios G. and Izzeldin, Marwan and Trapani, Lorenzo, Bayesian Estimation of Large Dimensional Time Varying VARs Using Copulas (December 27, 2019). Available at SSRN: https://ssrn.com/abstract=3510348 or http://dx.doi.org/10.2139/ssrn.3510348

Efthymios G. Tsionas

Lancaster University ( email )

Lancaster LA1 4YX
United Kingdom

Marwan Izzeldin

Lancaster University Management School ( email )

Lancaster, LA1 4YX
United Kingdom
01524 594674 (Phone)

HOME PAGE: http://www.lums.lancs.ac.uk/profiles/marwan-izzeldin/

Lorenzo Trapani (Contact Author)

affiliation not provided to SSRN

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