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Нечеткая Линейная Регрессия В Модели Роста Технологических Знаний // The Use Of Fuzzy Linear Regression In The Model Of Technological Knowledge Growth

Author

Listed:
  • E. Volkova S.

    (Financial University, Moscow)

  • V. Gisin B.

    (Financial University, Moscow)

  • Е. Волкова С.

    (Финансовый университет, Москва)

  • В. Гисин Б.

    (Финансовый университет, Москва)

Abstract

Fuzzy linear regression is used to estimate the parameters of the Botazzi-Peri model of technological knowledge growth, while classical linear regression is used to evaluate the parameters. In this paper, the parameters are defi ned as fuzzy values. As a result, the model volume of technological knowledge is characterized by a set of possible values rather than by a single number. The position in a series of possible values for a particular indicator characterizing a country, to some extent, refl ects the effectiveness of R & D in this country. If the fi gure is close to the most probable values of the model (when the value of the membership function is close to one), the effectiveness of R & D is quite typical. Closeness to peripheral values (when the value of the membership function is close to zero) occurs in two cases: if the effi ciency of R & D is atypically high or atypically low. The use of fuzzy regression allows to trace the dynamics of changes in effectiveness. The calculations were performed using the same data on body of technological knowledge in developed countries as in Botazzi and Peri work. Нечеткая линейная регрессия применяется для оценки параметров модели роста технологических знаний Ботацци—Пери, для оценки параметров применялась классическая линейная регрессия. В настоящей работе параметры определяются как нечеткие величины. В результате модельный объем технологических знаний характеризуется не единичным числом, а множеством возможных значений. Положение в ряду возможных значений конкретного показателя, характеризующего отдельную страну, в определенной мере отражает эффективность НИОКР в этой стране. Если показатель близок к наиболее вероятным модельным значениям (когда значение функции принадлежности близко к единице), эффективность НИОКР достаточно типична. Близость к периферийным значениям (когда значение функции принадлежности близко к нулю) возникает в двух случаях: если эффективность НИОКР нетипично высока или нетипично низка. Применение нечеткой регрессии позволяет проследить динамику изменения эффективности. Расчеты проведены на тех же данных, касающихся запасов технологических знаний в промышленно развитых странах, что и в работе Ботацци и Пери.

Suggested Citation

  • E. Volkova S. & V. Gisin B. & Е. Волкова С. & В. Гисин Б., 2015. "Нечеткая Линейная Регрессия В Модели Роста Технологических Знаний // The Use Of Fuzzy Linear Regression In The Model Of Technological Knowledge Growth," Финансы: теория и практика/Finance: Theory and Practice // Finance: Theory and Practice, ФГОБУВО Финансовый университет при Правительстве Российской Федерации // Financial University under The Government of Russian Federation, issue 5, pages 97-104.
    • Handle: RePEc:scn:financ:y:2015:i:5:p:97-104
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    References listed on IDEAS

    as
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