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Bayesian inference in a time varying cointegration model

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  • Koop, Gary
  • Leon-Gonzalez, Roberto
  • Strachan, Rodney W.

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 a new time varying parameter model which permits cointegration. We use 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 the Fisher effect.

Suggested Citation

  • Koop, Gary & Leon-Gonzalez, Roberto & Strachan, Rodney W., 2011. "Bayesian inference in a time varying cointegration model," Journal of Econometrics, Elsevier, vol. 165(2), pages 210-220.
  • Handle: RePEc:eee:econom:v:165:y:2011:i:2:p:210-220 DOI: 10.1016/j.jeconom.2011.07.007
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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. 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.
    5. 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.
    6. 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.
    7. 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.
    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 correction model; 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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