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Bayesian Inference in the Time Varying Cointegration Model

Author

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  • Gary Koop

    () (Department of Economics, University of Strathclyde)

  • Roberto Leon-Gonzalez

    () (National Graduate Institute for Policy Studies)

  • Rodney Strachan

    () (The Australian National University)

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 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.

Suggested Citation

  • Gary Koop & Roberto Leon-Gonzalez & Rodney Strachan, 2011. "Bayesian Inference in the Time Varying Cointegration Model," Working Papers 1121, University of Strathclyde Business School, Department of Economics.
  • Handle: RePEc:str:wpaper:1121
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Xianguo Huang & Roberto Leon-Gonzalez & Somrasri Yupho, 2012. "Financial Integration from a Time-Varying Cointegration Perspective," GRIPS Discussion Papers 12-07, National Graduate Institute for Policy Studies.
    5. Joshua Chan & Arnaud Doucet & Roberto Leon-Gonzalez & Rodney W. Strachan, 2018. "Multivariate Stochastic Volatility with Co-Heteroscedasticity," GRIPS Discussion Papers 18-12, National Graduate Institute for Policy Studies.
    6. John M. Maheu & Yong Song, 2018. "An efficient Bayesian approach to multiple structural change in multivariate time series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(2), pages 251-270, March.
    7. Jochmann Markus & Koop Gary, 2015. "Regime-switching cointegration," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(1), pages 35-48, February.
    8. Niko Hauzenberger & Florian Huber & Michael Pfarrhofer & Thomas O. Zorner, 2018. "Stochastic model specification in Markov switching vector error correction models," Papers 1807.00529, arXiv.org, revised Sep 2019.
    9. Xiaojie Xu, 2015. "Cointegration among regional corn cash prices," Economics Bulletin, AccessEcon, vol. 35(4), pages 2581-2594.
    10. CHAN Joshua & DOUCET Arnaud & Roberto Leon-Gonzalez & STRACHAN Rodney W., 2020. "Multivariate Stochastic Volatility with Co-Heteroscedasticity," GRIPS Discussion Papers 20-09, National Graduate Institute for Policy Studies.
    11. Martin Falk & Xiang Lin, 2018. "Income elasticity of overnight stays over seven decades," Tourism Economics, , vol. 24(8), pages 1015-1028, December.
    12. 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.
    13. 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.
    14. Funashima, Yoshito, 2020. "Money stock versus monetary base in time–frequency exchange rate determination," Journal of International Money and Finance, Elsevier, vol. 104(C).
    15. Barigozzi, Matteo, 2018. "On the stability of euro area money demand and its implications for monetary policy," LSE Research Online Documents on Economics 87283, London School of Economics and Political Science, LSE Library.
    16. Matteo Barigozzi & Antonio M. Conti, 2018. "On the Stability of Euro Area Money Demand and Its Implications for Monetary Policy," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 80(4), pages 755-787, August.
    17. Karolina Konopczak, 2020. "Kwantyfikacja zmian luki VAT: podejście ekonometryczne," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 2, pages 25-42.
    18. Pooyan Amir-Ahmadi & Christian Matthes & Mu-Chun Wang, 2016. "Choosing Prior Hyperparameters," Working Paper 16-9, Federal Reserve Bank of Richmond, revised 23 Aug 2016.
    19. Huber, Florian & Zörner, Thomas O., 2019. "Threshold cointegration in international exchange rates:A Bayesian approach," International Journal of Forecasting, Elsevier, vol. 35(2), pages 458-473.
    20. Ripamonti, Alexandre, 2013. "Rational Valuation Formula (RVF) and Time Variability in Asset Rates of Return," MPRA Paper 79460, University Library of Munich, Germany.
    21. 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.
    22. Czudaj, Robert L., 2019. "Dynamics between trading volume, volatility and open interest in agricultural futures markets: A Bayesian time-varying coefficient approach," Econometrics and Statistics, Elsevier, vol. 12(C), pages 78-145.

    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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