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FORECASTING GDP GROWTH USING DATA MINING ON THE EXAMPLE OF SERBIA

By
Rade Božić
Rade Božić

University of East Sarajevo, Faculty of Business Economics Bijeljina , Bijeljina , Bosnia and Herzegovina

Abstract

Predicting the outcome of various phenomena has always been an attractive research topic in a large number of scientific disciplines, especially in economics. As a scientific discipline, econometrics provides various models for predicting indicators such as GDP, inflation rate, interest rate, price of various goods and services, as well as many others at both micro and macro levels. The development of information technologies has made computational operations much faster and more precisely. However, a special contribution is reflected in the application of data mining for the purpose of extracting relevant information from a large data set. Models developed using data mining provide good results in predicting economic indicators, often more successfully than certain econometric models. This paper aims to forecast the growth of GDP through the application of time series mining on the example of the Republic of Serbia. The analysis was performed in two cases: in the first one models include independent attributes that additionally describe the dependent variable, while in the other case they do not contain these attributes. Three different mining methods were used in both cases (linear regression, multilayer perceptron and random forest) and the obtained results of model validation were presented and interpreted.

References

1.
Aggarwal C. Data Mining -The Textbook. 2015;
2.
Blanchard O. Macroeconomics. 2008;
3.
Brammer M. Principles of Data Mining. 2016;
4.
Breiman L. Random Forest. Machine learning. 2001;5–32.
5.
Carreiro A, Clark T, Marcellino M. Bayesian vars: specification choices and forecast accuracy. Jurnal of applied econometrics. 2015;46–73.
6.
Carreiro A, Clark T, Marcellino M. Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors. Journal of Econometrics. 2019;137–54.
7.
Carriero A, Galvao A, Kapetanios G. A comprehensive evaluation of macroeconomic forecasting methods. International Journal of Forecasting. 2019;1226–39.
8.
Chatfield C. 2016;
9.
Dean J. Data mining and machine learning, Value Creation for Business Leaders and Practitioners. 2014;
10.
Erkam G, Kayakutlu G, Daim T. Using artificial neural network models in stock market index prediction. Expert Systems with Applications. 2011;10389–97.
11.
Frank E, Hall M, Witten I, Pal C. The WEKA Workbench. 2016;
12.
Gérard B, Scornet E. A random forest guided tour. 2016;197–227.
13.
Hall M. Time Series Analysis and Forecasting with Weka -Pentaho Data Mining. 2014;
14.
Kriesel D. A Brief Introduction to Neural Networks. 2007;
15.
Limited F. 2022;
16.
Menzies T, Kocagüneli E, Peters F, Turhan B. 2015;321–53.
17.
Nemes M, Butoi A. Data Mining on Romanian Stock Market Using Neural Networks for Price. Informatica Economică. 2013;125–36.
18.
Schorfheide F, Song D. Real-Time Forecasting with a Mixed-Frequency VAR. Journal of Business & Economic Statistics. 2014;
19.
Shmueli G, Bruce C, Patel R. 2016;
20.
Smets F, Warne A, Wouters R. Professional forecasters and real-time forecasting with a DSGE model. International Journal of Forecasting. 2014;981–95.
21.
R. Republički zavod za statistiku. 2022;
22.
Stock H, Watson W. 2015;
23.
Tsai CF, Wu JW. Using neural network ensembles for bankruptcy prediction and credit scoring. Expert Systems with Applications. 2008;2639–49.
24.
Witten I, Frank E, Hall M, Pal C. 2017;

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