IDEAS home Printed from https://ideas.repec.org/a/scn/031101/15724755.html
   My bibliography  Save this article

Адаптивные Модели Прогнозирования Налоговых Показателей

Author

Listed:
  • Карабутов Николай Николаевич

    (Финансовый университет)

Abstract

Рассматриваются алгоритмы адаптивной идентификации дискретных систем с распределенным лагом. доказана ограниченность траекторий адаптивной системы идентификации. Полученные результаты применяются для построения адаптивных моделей прогнозирования изменения ВВП, а также объемов экспорта и импорта. Приведены результаты моделирования.The paper considers algorithms for adaptive identification of discrete systems with distributed lag. It shows the boundedness of trajectories in adaptive identification system. The results may be applied to build adaptive models for predicting changes in GDP, as well as in the volume of exports and imports. The results of simulation are presented.

Suggested Citation

  • Карабутов Николай Николаевич, 2014. "Адаптивные Модели Прогнозирования Налоговых Показателей," Экономика. Налоги. Право, CyberLeninka;Федеральное государственное образовательное бюджетное учреждение высшего профессионального образования «Финансовый университет при Правительстве Российской Федерации» (Финансовый университет), issue 6, pages 101-110.
    • Handle: RePEc:scn:031101:15724755
    as

    Download full text from publisher

    File URL: http://cyberleninka.ru/article/n/adaptivnye-modeli-prognozirovaniya-nalogovyh-pokazateley
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:scn:031101:15724755. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CyberLeninka (email available below). General contact details of provider: http://cyberleninka.ru/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.