VISUALIZATION CAPABILITIES OF SIMULATION OF ECONOMIC PROCESSES
DOI:
https://doi.org/10.17770/lner2016vol1.8.1479Keywords:
Matlab Simulink, modeling, simulation, teaching, visualizationAbstract
Educational experience shows that during the research process researchers perceive graphical information better than analytical relationships. Many economic courses operate with models that were previously available only in mathematics and physics disciplines. As a possible solution, there could be the use of the package Matlab Simulink in the realization of different algorithms both for engineering disciplines and economic studies. The article substantiates the usefulness of implementing the simulation models during the early stage of the research, when in parallel to acquiring analytical relations, simulation models may be introduced. The aim of the article is to show Matlab Simulink suitability for the purpose of visualizing simulation models of various economic disciplines. To reach the aim, the following research tasks have been set: identification of Matlab Simulink possibilities for simulation of economic processes; demonstrate visualization models on the basis of examples; visualization of time series model using Latgale unemployment rate data. The article presents examples of using simulation modeling in the economic research processes - optimal tax rate searching and time series application. Common research methods are used in this research: descriptive research method, statistical method, mathematical modeling.
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References
ANDERSON, P.L. (2005). Business Economics and Finance with MATLAB®, GIS, and Simulation Models. Chapman & Hall, 472 p.
CERNY, A. (2009). Mathematical Techniques in Finance: Tools for Incomplete Markets, 2e. Princeton University Press, 416 p.
CHAPRA, S.C., CANALE, R.P. (2015). Numerical Methods for Engineers, 7e. McGraw-Hill, 992 p.
CISAR, I. (2004). Simulink for economists, for Mathworks. Inc. Retrieved April 10, 2016, from http://matlab.exponenta.ru/simulink/book3/3.php
CSB (CENTRAL STATISTICAL BUREAU). (2016). Labour force survey in 1995-2014 (only in Latvian). Retrieved April 16, 2016, from http://www.csb.gov.lv/ en/dati/e-publikacijas/labour-force-survey-1995-2014-only-latvian-43358. html
KAY, C. (1984). Mathematics for Computer Programmers. New Jersey: Prentice Hall.
KAREL, P., TOMAS, Z. (2015). Multimedia Teaching Aid for Students of Basics of Control Theory in Matlab and Simulink. Procedia Engineering, Volume 100, 150–158.
KIRCHGASSNER, G., WOLTER, J. (2013). Introduction to modern time series analysis. Berlin: Springer, 320 p.
KIUSALAAS, J. (2016). Numerical Methods in Engineering with MATLAB, 3e. Cambridge University Press, 430 p.
LUTKEPOHHL, H. (2005). New introduction to multiple time series analysis. Berlin: Springer, 764 p.
MONTGOMERY, D.C., JOHNSON, L.A., GARDINER, J. (1990). Forecasting and time series analysis. Second edition. McGraw-Hill, Inc., New York, 381 p.
NEMBHARD, H.B., NEMBHARD D.A. (1996). Dynamic simulation for time series modeling. Proceedings of the 1996 Winter Simulation Conference.
SHIFLET, A.B., SHIFLET, G.W. (2014). Introduction to Computational Science: Modeling and Simulation for the Sciences, 2e. Princeton University Press, 856 p.
SIAUW, T., BAYERN, A. (2014). An Introduction to MATLAB Programming and Numerical Methods for Engineers. Academic Press, 340 p.
SILVA de, C.W., (2009). Modeling and Control of Engineering Systems. CRC Press, Inc, 766 p.
SMITH, D. (2013). Engineering Computation with MATLAB, 3e. Pearson Education Inc, 464 p.
TSAY, R.S. (2002). Analysis of financial time series. John Wiley & Sons, 448 p.
VLACHOS, M., GUNOPULOS, D. (2004). Indexing time series under condition of noise. Data mining in time series database: Series in machine perception and artificial intelligence. World Scientific Publishing, Vol.57, pp. 67-100.
XUE, D., CHEN, Y. (2013). System Simulation Techniques with MATLAB and Simulink. John Wiley & Sons, Inc, 488 p.
