Economic Analysis: Theory and Practice
 

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Developing a forecasting technique in complex economic systems simulation

Vol. 16, Iss. 3, MARCH 2017

PDF  Article PDF Version

Received: 11 March 2016

Received in revised form: 16 May 2016

Accepted: 25 July 2016

Available online: 29 March 2017

Subject Heading: MATHEMATICAL METHODS AND MODELS

JEL Classification: С15, С51

Pages: 573-581

https://doi.org/10.24891/ea.16.3.573

Bazhenov O.V. Ural Federal University named after the First President of Russia B.N. Yeltsin, Yekaterinburg, Russian Federation
6819@list.ru

Galenkova A.D. Ural Federal University named after the First President of Russia B.N. Yeltsin, Yekaterinburg, Russian Federation
agalenkova@mail.ru

Importance Under global economic instability, improvement of methods to assess various phenomena, simulate economic systems and make accurate forecasts is one of research priorities.
Objectives The aim of the study is to present theoretical and methodological tenets of the process of projection data generation on the state of a complex goal variable.
Methods We apply methods of least squares, partial least squares and exponential smoothing to present a procedure for complex economic systems modeling.
Results We present a methodology to forecast economic phenomena on a short-term horizon based on a comprehensive description of the index, using the least squares and partial least squares methods, and to generate projected values based on the method of exponential smoothing of explicative variables. The findings may be useful for commercial organizations and executive authorities to perform a strategic analysis, develop figures for indicative planning, and justify management decisions aimed at achieving the targets.
Conclusions and Relevance We offer to forecast explicative variables rather than the considered phenomenon itself, and develop a projected value of the phenomenon based on models designed under the LS and PLS-PM methods.

Keywords: forecasting, regression modeling, integrated economic system, PLS-PM, exponential smoothing

References:

  1. Zatonskii A.V., Sirotina N.A. [Forecasting the economic systems under a model based on regression differential equation]. Ekonomika i matematicheskie metody = Economics and Mathematical Methods, 2014, vol. 50, iss. 1, pp. 91–99. (In Russ.)
  2. Bazhenov O.V. [The structure and content of a strategic plan for development of a copper industry enterprise]. Rossiiskoe predprinimatel'stvo = Russian Journal of Entrepreneurship, 2013, no. 11, pp. 74–84. (In Russ.)
  3. Musatov M.V., L'vov A.A. [Analyzing the models of the least squares method and derivation of estimates estimates methods]. Vestnik Saratovskogo gosudarstvennogo tekhnicheskogo universiteta = Science Journal of Saratov State Technical University, 2009, vol. 4, iss. 2, pp. 137–140. (In Russ.)
  4. Vel'dyaksov V.N., Shvedov A.S. [On the method of least squares using regression with fuzzy data]. Ekonomicheskii zhurnal Vysshei shkoly ekonomiki = HSE Economic Journal, 2014, vol. 18, iss. 2, pp. 328–344. (In Russ.)
  5. Ringle C.M., Sarstedt M., Schlittgen R., Taylor C.R. PLS path modeling and evolutionary segmentation. Journal of Business Research, 2013, vol. 66, iss. 9, pp. 1318–1324. doi: 10.1016/j.jbusres.2012.02.031
  6. Athanasopoulou P., Giovanis A.N., Avlonitis G.J. Marketing strategy decisions for brand extension success. Journal of Brand Management, 2015, vol. 22, iss. 6, pp. 487–514. doi: 10.1057/bm.2015.27
  7. Castro I., Roldán J.L. A mediation model between dimensions of social capital. International Business Review, 2013, vol. 22, iss. 6, pp. 1034–1050. doi: 10.1016/j.ibusrev.2013.02.004
  8. Cepeda G., Martelo S., Barroso C., Ortega J. Integrating organizational capabilities to increase customer value: A triple interaction effect. In: New Perspectives in Partial Least Squares and Related Methods. Springer Proceedings in Mathematics & Statistics, 2013, no. 56, pp. 283–293.
  9. Ciavolino E., Nitti M. Using the hybrid two-step estimation approach for the identification of second-order latent variable models. Journal of Applied Statistics, 2013, vol. 40, iss. 3, pp. 508–526. doi: 10.1080/02664763.2012.745837
  10. Dijkstra T.K. PLS' Janus Face – Response to Professor Rigdon's ‘Rethinking Partial Least Squares Modeling: In Praise of Simple Methods’. Long Range Planning, 2014, vol. 47, iss. 3, pp. 146–153. doi: 10.1016/j.lrp.2014.02.004
  11. Hair J.F., Hult G.T.M., Ringle C.M., Sarstedt M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Los Angeles, SAGE, 2013, 329 p.
  12. Becker J.-M., Rai A., Ringle C.M., Völckner F. Discovering Unobserved Heterogeneity in Structural Equation Models to Avert Validity Threats. MIS Quarterly, 2013, vol. 37, iss. 3, pp. 665–694.
  13. Khazova D.S. [Modeling the sustainable development of tourism]. Мaterialy IV Mezhdunarodnoi nauchno-prakticheskoi konferentsii “Teoreticheskie i prikladnye aspekty sovremennoi nauki”. Chast' 2 [Proc. 4th Int. Sci. Conf. Theoretical and applied aspects of modern science. Part 2]. Belgorod, IP Petrova M.G. Publ., 2014, pp. 198–201. Available at: Link.
  14. Wold H. Estimation of Principal Components and Related Models by Iterative Least Squares. In: Multivariate Analysis II. New York, Academic Press, 1966, pp. 391–420.
  15. Wold H. Nonlinear Iterative Partial Least Squares (NIPALS) Modeling: Some Current Developments. In: Multivariate Analysis II. New York, Academic Press, 1973, pp. 383–407.
  16. Bazhenov O.V. [Building a PLS-PM model, characterizing the social significance of the copper industry enterprises (the Uralelectromed case)]. Tsvetnye metally = Non-ferrous Metals, 2016, no. 1, pp. 7–13. (In Russ.)
  17. Kerenskii A.M. [Exponential smoothing of the time series parameters in the presence of a trend]. Vestnik Samarskogo gosudarstvennogo aerokosmicheskogo universiteta im. akademika S.P. Koroleva (natsional'nogo issledovatel'skogo universiteta) = Vestnik of Samara University. Aerospace and Mechanical Engineering, 2011, no. 3-4, pp. 219–223. (In Russ.)
  18. Alekseeva I.Yu., Stepanov V.P., Vedernikov A.S. [Method of exponential smoothing of time series trend line in combination with the method of seasonality indices for short-term forecasting of power consumption]. Vestnik Samarskogo gosudarstvennogo tekhnicheskogo universiteta. Ser.: Tekhnicheskie nauki = Vestnik of Samara State Technical University. Technical Sciences Series, 2008, no. 1, pp. 137–143. (In Russ.)

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