FORECASTING GDP GROWTH USING DATA MINING ON THE EXAMPLE OF SERBIA

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

Keywords



Full Text


References


Aggarwal, C. C. (2015). Data Mining - The Textbook. London: Springer International Publishing .

Blanchard, O. (2008). Macroeconomics. Massachusetts: Pearson College Div.

Brammer, M. (2016). Principles of Data Mining. Oxford, UK: Springer-Verlag London.

Breiman, L. (2001). Random Forest. Machine learning, 5-32.

Carreiro, A., Clark, T., & Marcellino, M. (2015). Bayesian vars: specification choices and forecast accuracy. Jurnal of applied econometrics, 46-73.

Carreiro, A., Clark, T., & Marcellino, M. (2019). Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors. Journal of Econometrics, 137-154.

Carriero, A., Galvao, A. B., & Kapetanios, G. (2019). A comprehensive evaluation of macroeconomic forecasting methods. International Journal of Forecasting, 1226-1239.

Chatfield, C. (2016). The Analysis of Time Series: An Introduction. CRC Press.

Dean, J. (2014). Data mining and machine learning, Value Creation for Business Leaders and Practitioners. New Yersey: SAS Institute.

Erkam, G., Kayakutlu, G., & Daim, T. (2011). Using artificial neural network models in stock market index prediction. Expert Systems with Applications, 10389-10397.

Frank, E., Hall, M. A., Witten, I. H., & Pal, C. J. (2016). The WEKA Workbench. Retrieved from Waikato: https://waikato.github.io/weka-wiki/citing_weka/

Gérard, B., & Scornet, E. (2016). A random forest guided tour. Springer Nature SharedIt, 197-227.

Hall, M. (2014). Time Series Analysis and Forecasting with Weka - Pentaho Data Mining. Retrieved from https://pentaho-community.atlassian.net/wiki/spaces/DATAMINING/pages/293700841/Time+Series+Analysis+and+Forecasting+with+Weka#TimeSeriesAnalysisandForecastingwithWeka-3.2.2Lagcreation

Kriesel, D. (2007). A Brief Introduction to Neural Networks. Retrieved from https://www.dkriesel.com/_media/science/neuronalenetze-en-zeta2-2col-dkrieselcom.pdf

Limited, F. M. (2022). investing.com. Retrieved from https://www.investing.com/currencies/usd-rsd-historical-data

Menzies, T., Kocagüneli, E., Peters, F., & Turhan, B. (2015). Using Goals in Model-Based Reasoning. Sharing Data and Models in Software Engineering, 321-353.

Nemes, M., & Butoi, A. (2013). Data Mining on Romanian Stock Market Using Neural Networks for Price. Informatica Economică, 125-136.

Schorfheide, F., & Song, D. (2014). Real-Time Forecasting with a Mixed-Frequency VAR. Journal of Business & Economic Statistics.

Shmueli, G., Bruce, C. P., & Patel, R. N. (2016). Data Mining For Business Analytics, Concepts, Techniques, and Applications with XLMiner. New Yersey: John Wiley & Sons.

Smets, F., Warne, A., & Wouters, R. (2014). Professional forecasters and real-time forecasting with a DSGE model. International Journal of Forecasting, 981-995.

R. z. (2022). Republički zavod za statistiku. Retrieved from https://www.stat.gov.rs

Stock, H. J., & Watson, W. M. (2015). Introduction to econometrics - third edition. New Jersey: Pearson Education.

Tsai, C.-F., & Wu, J.-W. (2008). Using neural network ensembles for bankruptcy prediction and credit scoring. Expert Systems with Applications, 2639-2649.

Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2017). Data mining - Practical Machine Learning Tools and Techniques. Cambridge: Elsevier.




.