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Canonical correlation in multivariate time series analysis

Author

Listed:
  • Zaka Ratsimalahelo

    (LATEC - Laboratoire d'Analyse et de Techniques Economiques [UMR 5601] - UB - Université de Bourgogne - CNRS - Centre National de la Recherche Scientifique)

Abstract

We analyze a class o f state space identification algorithms for time series, based on canonical correlation analysis in the ligth of recent results on stochastic systems theory calle d « subspace methods » .These can be describe as covariance estimation followed b y stochastic realization . The methods offer the major advantage o f converting the nonlinear parameter estimation phase in traditional V A R M A models identification in to the solution o f Riccati equation but introduce at the same time some no n trivial mathematical problem s related to positivity. The states o f the forward - backward innovations representation have an interpretation : Instrumental Variables estimators .

Suggested Citation

  • Zaka Ratsimalahelo, 1997. "Canonical correlation in multivariate time series analysis," Working Papers hal-01526992, HAL.
    • Handle: RePEc:hal:wpaper:hal-01526992
    Note: View the original document on HAL open archive server: https://hal.science/hal-01526992
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