Bayesian Inference in the Time Varying Cointegration Model
AbstractThere 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 cointegration. 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 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 a permanent/transitory variance decomposition for inflation.
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Bibliographic InfoPaper provided by University of Strathclyde Business School, Department of Economics in its series Working Papers with number 1121.
Length: 49 pages
Date of creation: Apr 2011
Date of revision:
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More information through EDIRC
Bayesian; time varying cointegration; error correctionmodel; reduced rank regression; Markov Chain Monte Carlo.;
Other versions of this item:
- Gary Koop & Roberto Leon-Gonzalez & Rodney W. Strachan, 2008. "Bayesian Inference in the Time Varying Cointegration Model," Working Paper Series 23-08, The Rimini Centre for Economic Analysis, revised Jan 2008.
- Koop, Gary & Leon-Gonzalez, Roberto & Strachan, Rodney W., 2008. "Bayesian Inference in the Time Varying Cointegration Model," SIRE Discussion Papers 2008-60, Scottish Institute for Research in Economics (SIRE).
- Gary Koop & Roberto Leon Gonzalez & Rodney W. Strachan, 2008. "Bayesian Inference in the Time Varying Cointegration Model," GRIPS Discussion Papers 08-01, National Graduate Institute for Policy Studies.
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Longitudinal Data; Spatial Time Series
This paper has been announced in the following NEP Reports:
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Centoni, Marco & Cubadda, Gianluca, 2003. "Measuring the business cycle effects of permanent and transitory shocks in cointegrated time series," Economics Letters, Elsevier, vol. 80(1), pages 45-51, July.
- Jochmann, Markus & Koop, Gary, 2011.
SIRE Discussion Papers
2011-60, Scottish Institute for Research in Economics (SIRE).
- Jochmann, Markus & Koop, Gary, 2011. "Regime-Switching Cointegration," SIRE Discussion Papers 2011-36, Scottish Institute for Research in Economics (SIRE).
- Markus Jochmann & Gary Koop, 2011. "Regime-Switching Cointegration," Working Paper Series 40_11, The Rimini Centre for Economic Analysis.
- Markus Jochmann & Gary Koop, 2011. "Regime-Switching Cointegration," Working Papers 1125, University of Strathclyde Business School, Department of Economics.
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