Bayesian analysis of seasonally cointegrated VAR model

TL;DR

This paper develops a Bayesian framework for analyzing seasonally cointegrated VAR models with quarterly data, including prior structure, sampling schemes, and identification methods, demonstrated through simulations and economic data analysis.

Contribution

Introduces a Bayesian approach for seasonally cointegrated VAR models, addressing identification and estimation of cointegrating spaces with complex vectors.

Findings

Effective Bayesian estimation of cointegrating spaces

Successful application to Polish economic data

Simulation results validate the proposed methods

Abstract

The paper aims at developing the Bayesian seasonally cointegrated model for quarterly data. We propose the prior structure, derive the set of full conditional posterior distributions, and propose the sampling scheme. The identification of cointegrating spaces is obtained \emph{via} orthonormality restrictions imposed on vectors spanning them. In the case of annual frequency, the cointegrating vectors are complex, which should be taken into account when identifying them. The point estimation of the cointegrating spaces is also discussed. The presented methods are illustrated by a simulation experiment and are employed in the analysis of money and prices in the Polish economy.