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Parametric identification of the dynamics of inter-sectoral balance: modelling and forecasting

Author

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  • Olena Kostylenko
  • Helena Sofia Rodrigues
  • Delfim F. M. Torres

Abstract

This work is devoted to modelling and identification of the dynamics of the inter-sectoral balance of a macroeconomic system. An approach to the problem of specification and identification of a weakly formalized dynamical system is developed. A matching procedure for parameters of a linear stationary Cauchy problem with a decomposition of its upshot trend and a periodic component, is proposed. Moreover, an approach for detection of significant harmonic waves, which are inherent to real macroeconomic dynamical systems, is developed.

Suggested Citation

  • Olena Kostylenko & Helena Sofia Rodrigues & Delfim F. M. Torres, 2019. "Parametric identification of the dynamics of inter-sectoral balance: modelling and forecasting," Papers 1904.00029, arXiv.org.
  • Handle: RePEc:arx:papers:1904.00029
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    References listed on IDEAS

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    1. Imre Dobos & Adel Floriska, 2005. "A Dynamic Leontief Model with Non-renewable Resources," Economic Systems Research, Taylor & Francis Journals, vol. 17(3), pages 317-326.
    2. J. O. Ramsay & G. Hooker & D. Campbell & J. Cao, 2007. "Parameter estimation for differential equations: a generalized smoothing approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(5), pages 741-796, November.
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