Модифицированный метод идентификации логистической кривой Рамсея
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Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
Online media
issn 2310-6018

Identification of the Ramsay logistic curve by total least squares

idIvanov D.V.

UDC 519.254.1
DOI: 10.26102/2310-6018/2020.29.2.019

  • Abstract
  • List of references
  • About authors

Logistics curves are widely used in various fields of economics, technology, biology, chemistry. Estimating the parameters of logistic trends from the results of observations of the dynamic process in the economic system, with the aim of reliable analysis of economic indicators and predicting their future behavior, is one of the main tasks in the economy. One of the logistic models is the Ramsay function. The advantage of this function is the ability to use a linear difference equation to estimate its parameters. At the same time, non-linear data transformations are not required as for the logistics functions of Ferhulst or Gompertz. Modifications of a two-stage estimation algorithm based on the total least squares method and the extended instrumental variables method are proposed for estimating the parameters of the Ramsey curve.Tests have shown that the accuracy of parameter estimation using the proposed modifications is higher than the accuracy of the estimate obtained using the ordinary least squares method (LS).

1. Shields, L.B., Gertz, T.A., Wilson, K.C., Figg, G.L., Hester, S.T., & Honaker, J.T. The Scurve discontinuity theory predicts the path towards a “well” society and increased longevity. Medical Hypotheses, 2018;121:99-102. DOI:10.1016/j.mehy.2018.09.006.

2. Semenichev, V.K., Kurkin, E.I., Semenichev, E.V., Danilova, A.A., Fisun, G.A., & Kasatkina, E.I. Non-renewable recourses life cycles modeling aspects. Procedia Computer Science, 2015;65:872-879. DOI: 10.1016/j.procs.2015.09.046.

3. Tomić, D. Empirical evidence of an S-curve in Croatia. Ekonomska Istraživanja, 2019;32(1):2212-2230. DOI: 10.1080/1331677X.2019.1645718.

4. Semenychev, V.K., Korobetskaya, A.A., & Kozhukhova, V.N. Proposals of econometric tools for modeling and forecasting evolutionary processes: Monograph. Samara: SAGMU. 2015. (In Russ)

5. Ramsay, J.O.. A comparative study of several robust estimates of slope, intercept and scale in linear regression. Journal of the American Statistical Association,1977;72(359):608-615. DOI: 10.1080/01621459.1977.10480624.

6. Refaat, E.A. Lecture notes on z-transform. Morrisville: Lulu Press. 2005

7. Markovsky I., Van Huffel S. Overview of total least squares methods. Signal Processing, 200;87(10):2283–2302.

8. Ivanov, D.V., Chertykovtseva, N.V., Terekhova (Zharkova), A.A., & Andreeva, E.A. Identification of exponential trend models with fractional white noise. Journal of Physics Conference Series, 2019, 1368, 042061.DOI: 10.1088/1742-6596/1368/4/042061.

9. Söderström T., Stoica P. Instrumental Variable. Methods for System Identification. Berlin: Springer, 1983.

Ivanov Dmitriy Vladimirovich
PhD
Email: dvi85@list.ru

ORCID |

Samara State University Of Economics

Samara, Russian Federation

Keywords: total least square, logistic curve, ramsay function, estimation of parameters

For citation: Ivanov D.V. Identification of the Ramsay logistic curve by total least squares. Modeling, Optimization and Information Technology. 2020;8(2). URL: https://moit.vivt.ru/wp-content/uploads/2020/05/Ivanov_2_20_1.pdf DOI: 10.26102/2310-6018/2020.29.2.019 (In Russ).

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Published 30.06.2020