IDEAS home Printed from https://ideas.repec.org/a/scn/cememm/v52y2016i2p30-39.html
   My bibliography  Save this article

Определение Параметров Управления Региональным Развитием На Основе Алгоритмов Нечеткой Логики

Author

Listed:
  • Низамутдинов М.М.
  • Орешников В.В.

Abstract

В статье рассматриваются вопросы разработки стратегии регионального развития на основе инструментария экономико-математического моделирования социально-экономических систем. Предварительный анализ подходов и программных решений в данной сфере показал, что в первую очередь требуют рассмотрения аспекты определения количественных характеристик целей развития и выработки регулирующего воздействия для их достижения. Разработан подход к формированию индикативного плана и параметров управления в рамках обоснования среднесрочных стратегий регионального развития, интегрирующий процедуры целеполагания и регулирования, отличающийся нечетким алгоритмом классификации ситуаций и корректировкой значений параметров управления в условиях взаимной адаптации интересов различных подсистем региона. Для использования методов нечеткой логики были разработаны критерии оценки ситуации, функции принадлежности и база нечетких правил. Кроме того, сформирован комплекс правил корректировки управляющих параметров. Предложенный подход позволяет связать модель функционирования экономических агентов и модель определения целей регионального развития в единую систему управления с обратными связями, а также обеспечить наиболее эффективное использования ресурсов при формировании среднесрочной стратегии регионального развития.

Suggested Citation

  • Низамутдинов М.М. & Орешников В.В., 2016. "Определение Параметров Управления Региональным Развитием На Основе Алгоритмов Нечеткой Логики," Журнал Экономика и математические методы (ЭММ), Центральный Экономико-Математический Институт (ЦЭМИ), vol. 52(2), pages 30-39, апрель.
    • Handle: RePEc:scn:cememm:v:52:y:2016:i:2:p:30-39
    Note: Уфа
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    References listed on IDEAS

    as
    1. Gianni Amisano & John Geweke, 2017. "Prediction Using Several Macroeconomic Models," The Review of Economics and Statistics, MIT Press, vol. 99(5), pages 912-925, December.
    2. Andriantiatsaholiniaina, Luc A. & Kouikoglou, Vassilis S. & Phillis, Yannis A., 2004. "Evaluating strategies for sustainable development: fuzzy logic reasoning and sensitivity analysis," Ecological Economics, Elsevier, vol. 48(2), pages 149-172, February.
    3. Gary Fromm & Lawrence R. Klein & George R. Schink, 1972. "Short- and Long-Term Simulations with the Brookings Model," NBER Chapters, in: Econometric Models of Cyclical Behavior, Volumes 1 and 2, pages 201-310, National Bureau of Economic Research, Inc.
    4. Sherman Robinson, 2006. "Macro Models and Multipliers: Leontief, Stone, Keynes, and CGE Models," Economic Studies in Inequality, Social Exclusion, and Well-Being, in: Alain Janvry & Ravi Kanbur (ed.), Poverty, Inequality and Development, chapter 0, pages 205-232, Springer.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Calzolari, Giorgio & Ciriani, Tito A. & Corsi, Paolo, 1976. "Generation and testing of pseudo-random numbers to be used in the stochastic simulation of econometric models," MPRA Paper 24172, University Library of Munich, Germany.
    2. Miranda-Agrippino, Silvia & Ricco, Giovanni, 2018. "Bayesian Vector Autoregressions," The Warwick Economics Research Paper Series (TWERPS) 1159, University of Warwick, Department of Economics.
    3. Warne, Anders, 2023. "DSGE model forecasting: rational expectations vs. adaptive learning," Working Paper Series 2768, European Central Bank.
    4. Siddig, Khalid H.A. & Flaig, Dorothee & Luckmann, Jonas & Grethe, Harald, 2011. "A 2004 Social Accounting Matrix for Israel: Documentation of an Economy-Wide Database with a Focus on Agriculture, the Labour Market, and Income Distribution," Working Papers 110156, Universitaet Hohenheim, Institute of Agricultural Policy and Agricultural Markets.
    5. Knut Are Aastveit & Francesco Ravazzolo & Herman K. van Dijk, 2018. "Combined Density Nowcasting in an Uncertain Economic Environment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 131-145, January.
    6. Drachal, Krzysztof, 2021. "Forecasting crude oil real prices with averaging time-varying VAR models," Resources Policy, Elsevier, vol. 74(C).
    7. Philip B. Whyman, 2018. "The local economic impact of shale gas extraction," Regional Studies, Taylor & Francis Journals, vol. 52(2), pages 184-196, February.
    8. Tonnang, Henri E.Z. & Hervé, Bisseleua D.B. & Biber-Freudenberger, Lisa & Salifu, Daisy & Subramanian, Sevgan & Ngowi, Valentine B. & Guimapi, Ritter Y.A. & Anani, Bruce & Kakmeni, Francois M.M. & Aff, 2017. "Advances in crop insect modelling methods—Towards a whole system approach," Ecological Modelling, Elsevier, vol. 354(C), pages 88-103.
    9. Florian Huber & Gregor Kastner & Martin Feldkircher, 2019. "Should I stay or should I go? A latent threshold approach to large‐scale mixture innovation models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(5), pages 621-640, August.
    10. Hauber, Philipp, 2022. "Real-time nowcasting with sparse factor models," EconStor Preprints 251551, ZBW - Leibniz Information Centre for Economics.
    11. Todorov, Vladislav & Marinova, Dora, 2011. "Modelling sustainability," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(7), pages 1397-1408.
    12. Kenichiro McAlinn & Knut Are Aastveit & Jouchi Nakajima & Mike West, 2020. "Multivariate Bayesian Predictive Synthesis in Macroeconomic Forecasting," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(531), pages 1092-1110, July.
    13. Tan, Fei, 2018. "A Frequency-Domain Approach to Dynamic Macroeconomic Models," MPRA Paper 90487, University Library of Munich, Germany.
    14. Dixon, Peter B. & Rimmer, Maureen T., 2011. "You can't have a CGE recession without excess capacity," Economic Modelling, Elsevier, vol. 28(1-2), pages 602-613, January.
    15. Laura Liu, 2018. "Density Forecasts in Panel Data Models : A Semiparametric Bayesian Perspective," Finance and Economics Discussion Series 2018-036, Board of Governors of the Federal Reserve System (U.S.).
    16. Chiu, Ching-Wai (Jeremy) & Mumtaz, Haroon & Pintér, Gábor, 2017. "Forecasting with VAR models: Fat tails and stochastic volatility," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1124-1143.
    17. Francis X. Diebold & Minchul Shin & Boyuan Zhang, 2020. "On the Aggregation of Probability Assessments: Regularized Mixtures of Predictive Densities for Eurozone Inflation and Real Interest Rates," Papers 2012.11649, arXiv.org, revised Jun 2022.
    18. Suh, Hyunduk & Walker, Todd B., 2016. "Taking financial frictions to the data," Journal of Economic Dynamics and Control, Elsevier, vol. 64(C), pages 39-65.
    19. Tsoutsos, Theocharis & Drandaki, Maria & Frantzeskaki, Niki & Iosifidis, Eleftherios & Kiosses, Ioannis, 2009. "Sustainable energy planning by using multi-criteria analysis application in the island of Crete," Energy Policy, Elsevier, vol. 37(5), pages 1587-1600, May.
    20. R. V. Fattakhov & M. M. Nizamutdinov & V. V. Oreshnikov, 2020. "Assessment of the Attractiveness of Large Russian Cities for Residents, Tourists, and Business," Regional Research of Russia, Springer, vol. 10(4), pages 538-548, October.

    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:cememm:v:52:y:2016:i:2:p:30-39. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Sergei Parinov (email available below). General contact details of provider: http://www.cemi.rssi.ru/emm/home.htm .

    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.