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Recent Advances in Cointegration Analysis


  • Helmut LÜTKEPOHL


A small system of German economic variables consisting of the money stock M3, Gross National Product (GNP) and a bond rate is used to illustrate the power of cointegration analysis and the usefulness of some recently developed tools for this kind of analysis. Testing for the cointegrating rank and specifying a VECM, estimating the cointegrating relations and other parameters as well as model checking are discussed. The estimated model is analyzed with an impulse response analysis and a forecast error variance decomposition is performed. A quite reasonable long-run money demand relation is found.

Suggested Citation

  • Helmut LÜTKEPOHL, 2004. "Recent Advances in Cointegration Analysis," Economics Working Papers ECO2004/12, European University Institute.
  • Handle: RePEc:eui:euiwps:eco2004/12

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    References listed on IDEAS

    1. Keith Kuester & Volker Wieland, 2010. "Insurance Policies for Monetary Policy in the Euro Area," Journal of the European Economic Association, MIT Press, vol. 8(4), pages 872-912, June.
    2. Christiano, Lawrence J. & Trabandt, Mathias & Walentin, Karl, 2011. "Introducing financial frictions and unemployment into a small open economy model," Journal of Economic Dynamics and Control, Elsevier, vol. 35(12), pages 1999-2041.
    3. Del Negro, Marco & Schorfheide, Frank, 2008. "Forming priors for DSGE models (and how it affects the assessment of nominal rigidities)," Journal of Monetary Economics, Elsevier, vol. 55(7), pages 1191-1208, October.
    4. Thomas A. Lubik & Frank Schorfheide, 2004. "Testing for Indeterminacy: An Application to U.S. Monetary Policy," American Economic Review, American Economic Association, vol. 94(1), pages 190-217, March.
    5. Andrew T. Levin & Alexei Onatski & John Williams & Noah M. Williams, 2006. "Monetary Policy Under Uncertainty in Micro-Founded Macroeconometric Models," NBER Chapters,in: NBER Macroeconomics Annual 2005, Volume 20, pages 229-312 National Bureau of Economic Research, Inc.
    6. Marco Del Negro & Frank Schorfheide, 2009. "Monetary Policy Analysis with Potentially Misspecified Models," American Economic Review, American Economic Association, vol. 99(4), pages 1415-1450, September.
    7. Malin Adolfson & Stefan Laséen & Jesper Lindé & Lars E.O. Svensson, 2011. "Optimal Monetary Policy in an Operational Medium‐Sized DSGE Model," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43(7), pages 1287-1331, October.
    8. Robert E. Lucas Jr., 2003. "Macroeconomic Priorities," American Economic Review, American Economic Association, vol. 93(1), pages 1-14, March.
    9. Fagan, Gabriel & Henry, Jérôme & Mestre, Ricardo, 2001. "An area-wide model (AWM) for the euro area," Working Paper Series 0042, European Central Bank.
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    12. Fabio Canova, 2007. "Bayesian Analysis of DSGE Models by S. An and F. Schorfheide," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 187-192.
    13. Poirier, Dale J., 1998. "Revising Beliefs In Nonidentified Models," Econometric Theory, Cambridge University Press, vol. 14(04), pages 483-509, August.
    14. Cogley, Timothy & De Paoli, Bianca & Matthes, Christian & Nikolov, Kalin & Yates, Tony, 2011. "A Bayesian approach to optimal monetary policy with parameter and model uncertainty," Journal of Economic Dynamics and Control, Elsevier, vol. 35(12), pages 2186-2212.
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    Cited by:

    1. Leitao, Joao & Armada, Manuel Rocha & Ferreira, Joaaquim, 2012. "Corruption and Co-Movements in European Listed Sport Companies: Did Calciocaos really matter?," MPRA Paper 42474, University Library of Munich, Germany.
    2. Floriana Florestano, 2013. "Hydrocarbon exploitation and macroeconomic performance: a structural var approach for Basilicata1," ECONOMICS AND POLICY OF ENERGY AND THE ENVIRONMENT, FrancoAngeli Editore, vol. 2013(1), pages 147-174.

    More about this item


    cointegration; German monetary system; vector error correction model;

    JEL classification:

    • 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

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