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Bayesian Inference in the Time Varying Cointegration Model

Author

Listed:
  • Gary Koop

    (University of Strathclyde)

  • Roberto Leon Gonzalez

    (National Graduate Institute for Policy Studies)

  • Rodney W. Strachan

    () (School of Economics, University of Queensland)

Abstract

There are both theoretical and empirical reasons for believing that the pa- rameters 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 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.

Suggested Citation

  • 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.
  • Handle: RePEc:ngi:dpaper:08-01
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    References listed on IDEAS

    as
    1. 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.
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    Citations

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    Cited by:

    1. Punzi, Maria Teresa, 2016. "Financial cycles and co-movements between the real economy, finance and asset price dynamics in large-scale crises," FinMaP-Working Papers 61, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    2. Cross, Jamie & Nguyen, Bao H., 2017. "The relationship between global oil price shocks and China's output: A time-varying analysis," Energy Economics, Elsevier, vol. 62(C), pages 79-91.
    3. Maheu, John M & Song, Yong, 2017. "An Efficient Bayesian Approach to Multiple Structural Change in Multivariate Time Series," MPRA Paper 79211, University Library of Munich, Germany.
    4. Jochmann Markus & Koop Gary, 2015. "Regime-switching cointegration," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(1), pages 35-48, February.
    5. Panopoulou, Ekaterini & Pantelidis, Theologos, 2016. "The Fisher effect in the presence of time-varying coefficients," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 495-511.
    6. Matteo Barigozzi & Antonio Conti, 2013. "On the Stability of Euro Area Money Demand and its Implications for Monetary Policy," LEM Papers Series 2013/11, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    7. Shaun P Vahey & Elizabeth C Wakerly, 2013. "Moving towards probability forecasting," BIS Papers chapters,in: Bank for International Settlements (ed.), Globalisation and inflation dynamics in Asia and the Pacific, volume 70, pages 3-8 Bank for International Settlements.
    8. Xiaojie Xu, 2015. "Cointegration among regional corn cash prices," Economics Bulletin, AccessEcon, vol. 35(4), pages 2581-2594.
    9. Miroslav Plasil & Stepan Radkovsky & Pavel Rezabek, 2013. "Modelling bank loans to non-financial corporations," Occasional Publications - Chapters in Edited Volumes,in: CNB Financial Stability Report 2012/2013, chapter 0, pages 128-136 Czech National Bank, Research Department.
    10. Amir-Ahmadi, Pooyan & Matthes, Christian & Wang, Mu-Chun, 2016. "Choosing Prior Hyperparameters," Working Paper 16-9, Federal Reserve Bank of Richmond.
    11. Chew Lian Chua & Sarantis Tsiaplias, 2014. "A Bayesian Approach to Modelling Bivariate Time-Varying Cointegration and Cointegrating Rank," Melbourne Institute Working Paper Series wp2014n27, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.

    More about this item

    Keywords

    Bayesian; time varying cointegration; error correctionmodel; reduced rank regression; Markov Chain Monte Carlo.;

    JEL classification:

    • 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; Diffusion Processes; State Space Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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