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State Space Modelling of Cointegrated Systems using Subspace Algorithms

Author

Listed:
  • Segismundo Izquierdo

    (University of Valladolid)

  • Ces�reo Hern�ndez

    (University of Valladolid)

  • Javier Pajares

    (University of Valladolid)

Abstract

The use of subspace algorithms for the identification of non-stationary cointegrated stochastic systems is a promising technique that is currently under discussion. A revision of the literature provides two distinct algorithms: State Space Aoki Time Series (SSATS) identification algorithm (Aoki and Havenner 1991) and the Adapted Canonical Correlations Analysis (ACCA) of Bauer and Wagner (2002). Aoki's method is intuitively appealing, but lacks statistical foundation. In contrast, ACCA has a sound statistical basis, though intuition is somewhat lost. Both algorithms are revisited and commented. The study of the underlying ideas and properties of both previous algorithms leads us to propose a new method for subspace identification of non-stationary cointegrated stochastic systems, trying to combine the best features of each one. This new method provides a state space trend-cycle representation of a cointegrated system. Some preliminary simulation results are summarised, comparing these subspace methods with Johansen's maximum likelihood approach.

Suggested Citation

  • Segismundo Izquierdo & Ces�reo Hern�ndez & Javier Pajares, 2005. "State Space Modelling of Cointegrated Systems using Subspace Algorithms," Econometrics 0509010, University Library of Munich, Germany, revised 07 Feb 2006.
    • Handle: RePEc:wpa:wuwpem:0509010
    Note: Type of Document - pdf; pages: 11
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    References listed on IDEAS

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    1. Bauer, Dietmar & Wagner, Martin, 2002. "Estimating cointegrated systems using subspace algorithms," Journal of Econometrics, Elsevier, vol. 111(1), pages 47-84, November.
    2. Wagner, Martin, 1999. "VAR Cointegration in VARMA Models," Economics Series 65, Institute for Advanced Studies.
    3. José Mondéjar Jiménez & Manuel Vargas Vargas, 2006. "Análisis de tendencias comunes y cointegración en espacio de estados," Contribuciones a la Economía, Servicios Académicos Intercontinentales SL, issue 2006-09, September.
    4. Gonzalo, Jesus, 1994. "Five alternative methods of estimating long-run equilibrium relationships," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 203-233.
    5. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
    6. Dietmar Bauer & Martin Wagner, 2003. "The Performance of Subspace Algorithm Cointegration Analysis: A Simulation Study," Diskussionsschriften dp0308, Universitaet Bern, Departement Volkswirtschaft.
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    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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