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A Comparison of Johansen's, Bierens and the Subspace Algorithm Method for Cointegration Analysis

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  • Martin Wagner

Abstract

The methods listed in the title are compared by means of a simulation study and a real world application. The aspects compared in the simulations are: The performance of the tests of the different methods for the dimension of the cointegrating space and the quality of the estimated cointegrating space. It turns out that the subspace algorithm method, formulated in the state space framework and thus applicable for ARMA processes, performs at least comparable to the Johansen procedure and both perform significantly better than Bierens' method. The real world application is an investigation of the long-run properties of the neoclassical growth model for Austria. It turns out that the results do not fully support the theoretical predictions and that they are very versatile across the employed methods. The degree of versatility depends strongly upon the number of variables. For the case of 6 variables and about 100 observations huge differences occur, which lead us to conclude that the results of this typical situation in the applied literature should be interpreted with more caution than is commonly done.

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Paper provided by Universitaet Bern, Departement Volkswirtschaft in its series Diskussionsschriften with number dp0210.

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Date of creation: Oct 2002
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Handle: RePEc:ube:dpvwib:dp0210

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Keywords: Cointegration; State Space Models; Subspace Algorithms; Simulation; Neoclassical Growth Model;

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References

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  1. King, Robert G. & Plosser, Charles I. & Rebelo, Sergio T., 1988. "Production, growth and business cycles : II. New directions," Journal of Monetary Economics, Elsevier, vol. 21(2-3), pages 309-341.
  2. Dietmar Bauer & Martin Wagner, 2003. "The Performance of Subspace Algorithm Cointegration Analysis: A Simulation Study," Diskussionsschriften dp0308, Universitaet Bern, Departement Volkswirtschaft.
  3. Kunst, Robert & Neusser, Klaus, 1990. "Cointegration in a Macroeconomic System," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 5(4), pages 351-65, Oct.-Dec..
  4. Phillips, P C B, 1991. "Optimal Inference in Cointegrated Systems," Econometrica, Econometric Society, vol. 59(2), pages 283-306, March.
  5. Hyllerberg, S. & Engle, R.F. & Granger, C.W.J. & Yoo, B.S., 1988. "Seasonal Integration And Cointegration," Papers 0-88-2, Pennsylvania State - Department of Economics.
  6. Klaus NEUSSER, 1990. "Testing the Long-Run Implications of the Neoclassical Growth Model," Vienna Economics Papers vie9002, University of Vienna, Department of Economics.
  7. Ripatti, Antti & Saikkonen, Pentti, 1998. "Cointegrated Vector Autoregressive Processes with Continuous Structural Changes," Research Discussion Papers 29/1998, Bank of Finland.
  8. Peter C.B. Phillips, 1993. "Fully Modified Least Squares and Vector Autoregression," Cowles Foundation Discussion Papers 1047, Cowles Foundation for Research in Economics, Yale University.
  9. Saikkonen, Pentti, 1992. "Estimation and Testing of Cointegrated Systems by an Autoregressive Approximation," Econometric Theory, Cambridge University Press, vol. 8(01), pages 1-27, March.
  10. H. Peter Boswijk & Andre Lucas & Nick Taylor, 1999. "A Comparison of Parametric, Semi-nonparametric, Adaptive, and Nonparametric Cointegration Tests," Tinbergen Institute Discussion Papers 99-012/4, Tinbergen Institute.
  11. repec:cup:etheor:v:8:y:1992:i:1:p:1-27 is not listed on IDEAS
  12. Dietmar Bauer & Martin Wagner, 2002. "A Canonical Form for Unit Root Processes in the State Space Framework," Diskussionsschriften dp0204, Universitaet Bern, Departement Volkswirtschaft.
  13. Dietmar Bauer & Martin Wagner, 2000. "Estimating Cointegrated Systems Using Subspace Algorithms," Econometric Society World Congress 2000 Contributed Papers 0293, Econometric Society.
  14. Wagner, Martin, 1999. "VAR Cointegration in VARMA Models," Economics Series 65, Institute for Advanced Studies.
  15. Bierens, H.J., 1995. "Nonparametric cointegration analysis," Discussion Paper 1995-123, Tilburg University, Center for Economic Research.
  16. Dietmar Bauer & Martin Wagner, 2002. "Asymptotic Properties of Pseudo Maximum Likelihood Estimates for Multiple Frequency I(1) Processes," Diskussionsschriften dp0205, Universitaet Bern, Departement Volkswirtschaft.
  17. Saikkonen, Pentti & Luukkonen, Ritva, 1997. "Testing cointegration in infinite order vector autoregressive processes," Journal of Econometrics, Elsevier, vol. 81(1), pages 93-126, November.
  18. Seo, Byeongseon, 1998. "Tests For Structural Change In Cointegrated Systems," Econometric Theory, Cambridge University Press, vol. 14(02), pages 222-259, April.
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Cited by:
  1. Martin Wagner & Jaroslava Hlouskova, 2010. "The Performance of Panel Cointegration Methods: Results from a Large Scale Simulation Study," Econometric Reviews, Taylor & Francis Journals, vol. 29(2), pages 182-223.
  2. Izquierdo, Segismundo S. & Hernández, Cesáreo & del Hoyo, Juan, 2006. "Forecasting VARMA processes using VAR models and subspace-based state space models," MPRA Paper 4235, University Library of Munich, Germany.

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