Modelling Nonlinear Nonstationary Processes in Macroeconomy and Finances

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 754)

Abstract

Modern decision support systems need the methods of predictive modelling, which would allow create models of systems with given parameters, such that they are capable of being promptly subjected to changes and additions. It could be used to deal with uncertainties of different types, to maximize the automation of the process of constructing predictive models and improve the quality of forecasts estimated. This article is devoted to the study and solving the problem of modeling and forecasting nonlinear nonstationary processes in economy and finances using the methodology proposed based on systemic approach to model structure and parameter estimation. We present preliminary data processing techniques necessary for eliminating possible uncertainties, application of data correlation analysis for model structure estimation, and a set of model parameter estimation methods providing a possibility for computing unbiased estimates of parameters. Proposed methodology can be applied in decision support systems used in finance and economy spheres under conditions of various uncertainties and risks that usually take place in modeling and forecasting using statistical data. In the present paper we will describe in short the usage of the methodology of adaptive modelling and give a couple of examples presenting new results of its application for forecasting behavior of several economic and financial processes.

Keywords

Nonlinear nonstationary process Uncertainties Modelling Forecasting Macroeconomy Finances 

References

  1. 1.
    Trofymchuk, O., et al.: Probabilistic and statistical uncertainty Decision Support Systems. Visnyk Lviv Polytech. Natl Univ. 826, 237–248 (2015)Google Scholar
  2. 2.
    Sallam, E., Medhat, T., Ghanem, A., Ali, M.E.: Handling numerical missing values via rough sets. Int. J. Math. Sci. Comput. (IJMSC) 3(2), 22–36 (2017).  https://doi.org/10.5815/ijmsc.2017.02.03CrossRefGoogle Scholar
  3. 3.
    Diebold, F.X.: Forecasting in Economics, Business, Finance and Beyond. University of Pennsylvania, Philadelphia (2015)Google Scholar
  4. 4.
    Hansen, B.E.: Econometrics. University of Wisconsin, Madison (2017)Google Scholar
  5. 5.
    Tsay, R.S.: Analysis of Financial Time Series. Wiley, New York (2010)CrossRefGoogle Scholar
  6. 6.
    Dovgij, S.O., Trofymchuk, O.M., Bidyuk, P.I.: DSS Based on Statistical and Probabilistic Procedures. Logos, Kyiv (2014)Google Scholar
  7. 7.
    Shah, Y.A., Mir, I.A., Rathea, U.M.: Quantum mechanics analysis: modeling and simulation of some simple systems. Int. J. Math. Sci. Comput. (IJMSC) 2(1), 23–40 (2016).  https://doi.org/10.5815/ijmsc.2016.01.03CrossRefGoogle Scholar
  8. 8.
    Ramsey, J.B.: Tests for specification errors in classical linear least squares regression analysis. J. Roy. Stat. Soc. B 31, 350–371 (1969)MathSciNetMATHGoogle Scholar
  9. 9.
    Terasvirta, T., Tjostheim, D., Granger, C.W.J.: Aspects of modeling nonlinear time series. In: Engle, R.F., McFadden, D.L. (eds.) Handbook of Econometrics, vol. 4, pp. 2919–2957 (1994)Google Scholar
  10. 10.
    Tsay, R.S.: Nonlinearity tests for time series. Biometrika 73, 461–466 (1986)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Tjostheim, D.: Some doubly stochastic time series models. J. Time Ser. Anal. 7, 51–72 (1986)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Tjostheim, D.: Nonlinear time series: a selective review. Scand. J. Stat. 21(2), 97–130 (1994)MATHGoogle Scholar
  13. 13.
    Krishna, G.V.: Prediction of rainfall using unsupervised model based approach using K-means algorithm. Int. J. Math. Sci. Comput. (IJMSC) 1(1), 11–20 (2015).  https://doi.org/10.5815/ijmsc.2015.01.02CrossRefGoogle Scholar
  14. 14.
    Khashei, M., Montazeri, M.A., Bijari, M.: Comparison of four interval ARIMA-base time series methods for exchange rate forecasting. Int. J. Math. Sci. Comput. (IJMSC) 1(1), 21–34 (2015).  https://doi.org/10.5815/ijmsc.2015.01.03CrossRefGoogle Scholar
  15. 15.
    Stensholt, B.K., Tjostheim, D.: Multiple bilinear time series models. J. Time Ser. Anal. 8, 221–233 (1987)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Tsay, R.S.: Testing and modeling threshold autoregressive processes. J. Am. Stat. Assoc. 84, 231–240 (1989)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Aminu, E.F., Ogbonnia, E.O., Shehu, I.S.: A predictive symptoms-based system using support vector machines to enhanced classification accuracy of Malaria and Typhoid coinfection. Int. J. Math. Sci. Comput. (IJMSC) 2(4), 54–66 (2016).  https://doi.org/10.5815/ijmsc.2016.04.06CrossRefGoogle Scholar
  18. 18.
    Anderson, W.N., Kleindorfer, G.B., Kleindorfer, P.R., Woodroofe, M.B.: Consistent estimates of the parameters of a linear system. Ann. Math. Stat. 40(6), 2064–2075 (1969)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Yavin, Y.A.: Discrete Kalman filter for a class of nonlinear stochastic systems. Int. J. Syst. Sci. 3(11), 1233–1246 (1982)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Eeckhoudt, L., Gollier, C., Schlesinger, H.: Economic and Financial Decisions Under Uncertainty. Princeton University Press, Princeton (2004)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute for Applied System Analysis NTUU “Igor Sikorsky Kyiv Polytechnic Institute”KyivUkraine
  2. 2.Institute of Telecommunications and Global Information Space of the National Academy of Sciences of UkraineKyivUkraine

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