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Forecasting the main economic indicators for industry in the analytical system "Horizon"

Author

Listed:
  • Kitova, Olga
  • Dyakonova, Ludmila
  • Savinova, Victoria
  • Fomin, Kiril

Abstract

Industrial development is of great strategic importance for ensuring sustainable economic growth in Russia and solving social problems. Therefore, the development of approaches and methods for comprehensive analysis and forecasting of industrial indicators at the national and regional level is of particular importance, which will facilitate the adoption of scientifically based decisions in the field of industrial planning and management. What is needed is a system of indicator models that will allow for a comprehensive analysis of industrial development, identification of the main influencing factors, as well as the development of forecasting models and methods and their application to the indicators under study. The hybrid forecasting system “Horizon”, developed by the authors of the study, implements regression and intelligent models for most groups of indicators of the Russian economy. At the same time, most researchers rely in their studies on autoregressive time series models based on ARIMA. The authors have developed a new module of the Horizon ARIMA system, which can be used when forecasting individual indicators. These forecasts can be considered as baseline when conducting comparative analysis with hybrid models. This study is devoted to forecasting a group of main economic indicators of Russian industry using ARIMA time series models.

Suggested Citation

  • Kitova, Olga & Dyakonova, Ludmila & Savinova, Victoria & Fomin, Kiril, 2023. "Forecasting the main economic indicators for industry in the analytical system "Horizon"," MPRA Paper 118887, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:118887
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    References listed on IDEAS

    as
    1. Dougherty, Christopher, 2011. "Introduction to Econometrics," OUP Catalogue, Oxford University Press, edition 4, number 9780199567089.
    2. Fokin, Nikita (Фокин, Никита), 2019. "VAR-LASSO model for the Russian economy on a large data set [Var-Lasso Модель Для Российской Экономики На Большом Массиве Данных]," Working Papers 031951, Russian Presidential Academy of National Economy and Public Administration.
    3. Pestova, Anna (Пестова, Анна) & Mamonov, Mikhail (Мамонов, Михаил), 2016. "Estimating the Influence of Different Shocks on Macroeconomic Indicators and Developing Conditional Forecasts on the Basis of BVAR Model for the Russian Economy [Оценка Влияния Различных Шоков На Д," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 4, pages 56-92, August.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    socio-economic indicators of the Russian Federation; industry indicators; forecasting; time series; hybrid information and analytical system;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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