Bayesian Estimation of Large Dimensional Time Varying VARs Using Copulas
32 Pages Posted: 17 Jan 2020
Date Written: December 27, 2019
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: Suggested Citation