Features of application of polynomial regression model with measurement errors in forecasting of socio-economic processes.
Keywords:
regression model, errors of measurement, estimator, least squares method, econometric model, multicollinearity.
Abstract
A polynomial regression model with measurement errors is considered. Using the method of corrected least squares, a consistent estimate for an unknown parameter was found. The relationship between factor variables that could potentially be included in the econometric model is investigated. The influence of multicollinearity on the quality of estimators obtained with the help of MLS is substantiated. Methods of multicollinearity detection and multicollinearity testing methods are considered.
References
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Khomyak M. Ya. Application of the polynomial model with errors in variables in forecasting socio-economic processes // Modern challenges and current problems of science, education and production: intersectoral debates [collection. Science. pr.]: materials of the IX International Scientific and Practical Internet Conference (Kyiv, October 16, 2020). Kyiv, 2020. - P. 723-726.
Khomyak M. A polynomial errors-in-variables model in forecasting of economic processes // International Scientific Internet Conference "Information Society: Technological, Economic and Technical Aspects of Formation" (issue 52) / Collection of abstracts: issue 52 (m. Ternopil, October 14, 2020). - Ternopil. - P. 17-19.
Economic and mathematical modeling: Textbook / Ed. OT Ivashchuk. Ternopil: TNEU "Economic Thought", 2008. 704 p.
Kukush A.G. and Polekha M.Ya. A goodness-of-fit test for a multivariate errors-in-variables model. Theory of Stochastic Processes. 12(28), 3-4, 2006. 67-79.
The method of principal components. Wikipedia is a free encyclopedia. URL: https://uk.wikipedia.org/wiki/Метод_головних_компонент (access date: 03.11.2020).
Moiseev NA Comparative analysis of the effectiveness of methods to eliminate multicollinearity. Accounting and statistics. 2017. № 2. S. 62–73.
Paul R.K. Multicollinearity: causes, effects and remedies. IASRI. 2006. No. 35. P. 58–65.
Cheng C.-L. and Schneeweiss H. Polynomial regression with errors in the variables. J. R. Statist. Soc. B, 60, 1998. 189-199.
Cheng C.-L. and Schneeweiss H. A small sample estimator for a polynomial regression with errors in the variables. J. R. Statist. Soc. B, 62, 2000. 699-709.
Cheng C.-L. and Schneeweiss H. On the polynomial measurement error model, in: S. van Huffel and P. Lemmerling (editors), Total Least Squares and Errors-in-Variables Modeling. Kluwer, Dordrecht, 2002. 131-143.
Khomyak M. Ya. Application of the polynomial model with errors in variables in forecasting socio-economic processes // Modern challenges and current problems of science, education and production: intersectoral debates [collection. Science. pr.]: materials of the IX International Scientific and Practical Internet Conference (Kyiv, October 16, 2020). Kyiv, 2020. - P. 723-726.
Khomyak M. A polynomial errors-in-variables model in forecasting of economic processes // International Scientific Internet Conference "Information Society: Technological, Economic and Technical Aspects of Formation" (issue 52) / Collection of abstracts: issue 52 (m. Ternopil, October 14, 2020). - Ternopil. - P. 17-19.
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Published
2020-12-21
How to Cite
Khomyak, M. (2020). Features of application of polynomial regression model with measurement errors in forecasting of socio-economic processes. COMPUTER-INTEGRATED TECHNOLOGIES: EDUCATION, SCIENCE, PRODUCTION, (41), 114-118. https://doi.org/10.36910/6775-2524-0560-2020-41-19
Section
Automation and Control
