Bayesian inference in the time varying cointegration model

G.M. Koop, Roberto Leon-Gonzalez, Rodney W. Strachan

Research output: Working paper

49 Downloads (Pure)


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 cointegration. This is despite the fact that cointegration plays an important role in informing macroeconomists on a range of issues. In this paper we develop time varying parameter models which permit coin-tegration. Time-varying parameter VARs (TVP-VARs) typically use state space representations to model the evolution of parameters. In this paper, we show that it is not sensible to use straightforward extensions of TVP-VARswhen allowing for cointegration. Instead we develop a specication 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 a permanent/transitory variance decomposition for inflation.
Original languageEnglish
Place of PublicationGlasgow
PublisherUniversity of Strathclyde
Number of pages48
Publication statusUnpublished - May 2008


  • bayesian
  • time varying cointegration
  • error correction model
  • reduced rank regression
  • markov chain
  • monte carlo method


Dive into the research topics of 'Bayesian inference in the time varying cointegration model'. Together they form a unique fingerprint.

Cite this