Bayesian Inference in a Time Varying Cointegration Model

Abstract

There are both theoretical and empirical reasons for believing that the parameters of macroeconomic models may vary over time. However, work with time-varying parameter models has largely involved Vector autoregressions (VARs), ignoring cointegrations. This is despite the fact that cointegration plays an important role in informing macroeconomists on a range of issues. In this paper we develop a new time varying parameter model which permits cointegration. We use a specification which allows for the cointegrating space to evolve over time in a manner comparable to the random walk variation used with TVP-VARs. The properties of our approach are investigated before developing a method of posterior simulation. We use our methods in an empirical investigation involving the Fisher effect.

Keywords: Bayesian, time varying cointegration, error correction model, reduced rank regression, Markov Chain Monte Carlo

JEL Classification: C11, C32, C33