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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.

Suggested Citation

  • Martin Wagner, 2002. "A Comparison of Johansen's, Bierens and the Subspace Algorithm Method for Cointegration Analysis," Diskussionsschriften dp0210, Universitaet Bern, Departement Volkswirtschaft.
    • Handle: RePEc:ube:dpvwib:dp0210
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    References listed on IDEAS

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    1. Phillips, Peter C B, 1995. "Fully Modified Least Squares and Vector Autoregression," Econometrica, Econometric Society, vol. 63(5), pages 1023-1078, September.
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    9. Dietmar Bauer & Martin Wagner, 2002. "A Canonical Form for Unit Root Processes in the State Space Framework," Diskussionsschriften dp0204, Universitaet Bern, Departement Volkswirtschaft.
    10. Dietmar Bauer & Martin Wagner, 2002. "Asymptotic Properties of Pseudo Maximum Likelihood Estimates for Multiple Frequency I(1) Processes," Diskussionsschriften dp0205, Universitaet Bern, Departement Volkswirtschaft.
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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, April.
    2. Martin Wagner, 2010. "Cointegration analysis with state space models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 94(3), pages 273-305, September.
    3. 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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    More about this item

    Keywords

    Cointegration; State Space Models; Subspace Algorithms; Simulation; Neoclassical Growth Model;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: 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
    • E20 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - General (includes Measurement and Data)

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