VEC-MSF models in Bayesian analysis of short- and long-run relationships

What is it about?

The paper aims at developing new Bayesian Vector Error Correction – Stochastic Volatility (VEC-SV) models, which combine the VEC representation of a VAR structure with stochastic volatility, represented by either the multiplicative stochastic factor (MSF) process or the MSF-SBEKK specification. Appropriate numerical methods (MCMC-based algorithms) are adapted for estimation and comparison of these type of models.

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Why is it important?

The paper is focused on the construction and estimation of Bayesian Vector Error Correction -Stochastic Volatility (VEC-SV) models. As regards the stochastic volatility part of the specification we start with the MSF structure, which is the simplest among the MSV structures, thereby allowing a parsimonious way of modelling time-variability of volatility. Such a framework enables us to capture the long-run relationships among processes, and also to formally examine the presence of time-variation in the conditional covariance matrix.