Bayesian inference in the time varying cointegration model

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

Research output: Working paper

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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 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-VARs when allowing for cointegration. Instead we develop a speci…cation 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

Keywords

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

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    Koop, G. M., Leon-Gonzalez, R., & Strachan, R. W. (2008). Bayesian inference in the time varying cointegration model. University of Strathclyde.