Development of a new model of gross domestic product forecasting

Year 2021, Volume: 5 Issue: 1, 564 - 575, 30.06.2021

Yusuf Tansel İç , Hakan Civelek

Abstract

Economic growth is usually calculated as the increase in real Gross Domestic Product (GDP). Estimation of economic growth is made by countries or international organizations in order to predict the future cycle of the economy of a country. Thus, decision makers will be able to develop early policies against future situations. In this study, factorial designs, one of the experimental design methods, is used to estimate economic growth. It is observed that time series analysis and econometric methods are frequently used in the determination of the factors affecting economic growth and growth estimation studies. For the analysis, using correlation analysis among the factors that are considered to be ineffective on growth are eliminated, and correlation of the inflation rate, unemployment rate, industrial production index, foreign trade volume to GDP ratio, and the ratio of gross external debt stock to GDP are considered as factors in the analysis. The rate of change in GDP is taken into account as output. As a result of the analysis, a regression model is determined. When the regression model is provided, the novel forecasting model can be easily obtained. It is different from the conventional forecasting models that require the complex statistical evaluations. In this study, we present a novel 2k factorial design methodology in order to solve the GDP forecasting problem. Furthermore, we propose a general framework of the presented model in the econometrics perspectives, a numerical solution to illustrate this demonstration as well.

Keywords

Economics, forecasting, design of experiments, economic growth, factorial design

References

• ADOFSON, M., LASEEN, S., LINDE, J. and VILLANI, M. (2007). Bayesian Estimation of an Open Economy DSGE Model With Incomplete Pass-through, Journal of International Economics, 72(2),481-511.
• AMIRAT, A., ZAIDI, M. (2020). Estimating GDP Growth in Saudi Arabia under the Government’s Vision 2030: a Knowledge-based Economy Approach. J Knowl Econ (In Press).
• ANTONY, J. and CAPON, A. (1998). Teaching Experimental Design Techniques to Industrial Engineers”, Int. J. Engng Ed., 14(5), 335-343. Central Bank of Republic of Turkey (2017). https://evds2.tcmb.gov.tr/index.php?/evds/serieMarket/ Accessed 10 March 2017.
• CERQUEIRA, L.F., PIZZINGA, A. & FERNANDES, C. (2009).Methodological Procedure for Estimating Brazilian Quarterly GDP Series. Int Adv Econ Res 15, 102–114.
• DAS, S., COONDOO, D. (2018). Is PMI Useful in Quarterly GDP Growth Forecasts for India? An Exploratory Note. J. Quant. Econ. 16, 199–207. https://doi.org/10.1007/s40953-017-0116-1
• DENGİZ, B., İÇ, Y.T., BELGİN, Ö. (2016). A Meta-Model Based Simulation Optimization Using Hybrid Simulation-Analytical Modeling to Increase The Productivity In Automotive Industry, Mathematics and Computers in Simulation 120, 109-128.
• DIAS, F. and PINHEIRO, M., RUA, A., (2015). Forecasting Portuguese GDP with Factor Models: Pre- and Post-crisis Evidence, Economic Modelling, 44(C), 266-272.
• DUA, P. Macroeconomic Modelling and Bayesian Methods. J. Quant. Econ. 15, 209–226 (2017). https://doi.org/10.1007/s40953-017-0077-4
• DÜLGER, E. (2016). Ekonomide Öncü Göstergeler ile Büyüme Tahmini Uygulaması, Yüksek Lisans Tezi, Ankara Üniversitesi Fen Bilimleri Enstitüsü Bilgisayar Mühendisliği Anabilim Dalı, Ankara, Turkey (In Turkish).
• FAIR, R.C. and PARKE, W.R. (1980). Full-Information Estimates Of A Nonlinear Macroeconometric Model, Journal of Econometrics, 13(3), 269-291.
• FENG, L. and ZHANG, J. (2014). Application of Artificial Neural Networks in Tendency Forecasting of Economic Growth, Economic Modelling, 40, 76-80.
• FEUERRIEGEL, S., GORDON, J. (2019). News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions. European Journal of Operational Research 272, 162–175.
• FERRAINI, B., SCARAMOZZINO, P. (1971). Production comlexity, Adaptability and Economic Growth, SOMMERS, P. M. and SUITS, D.B., A Cross-Section Model of Economic Growth, The Review of Economics and Statistics,.53(2) 121-128.
• HEIBERGER, R. H., (2017). Predicting Economic Growth with Stock Networks, Physica A: Statistical Mechanics and its Applications, 489, 102-111.
• HINKELMANN, K. and KEMPTHORNE, O. (2008). Design and Analysis of Experiments Volume 1, Sec. Ed., Introduction to Experimental Design, A John Wiley & Sons, Inc., New Jersey, USA.
• KLEIJNEN, J.P.C. and SARGENT, R.G. (2000). A Methodology for Fitting and Validating Meta-Models in Simulation, European Journal of Operational Research, 120,14–29.
• KRKOSKA, L. and TEKSOZ, U. (2009). How Reliable Are Forecast of GDP Growth and Inflation For Countries with Limited Coverage? Economic Systems, 33(4), 376-388.
• MAKSIMOVIC, G., JOVIC, S. and JOVANOVIC, R. (2016). Economic Growth Rate Management by Soft Computing Approach, Physica A: Statistical Mechanics and its Applications, 464, 520-524.
• MARKOVIC, D., PETKOVIC, D., NIKOLIC, V., MILOVANCEVIC, M. and PETKOVIC, B. (2017). Soft Computing Prediction of Economic Growth Based in Science and Technology Factors, Physica A: Statistical Mechanics and its Applications, 465, 217-220.
• MONTGOMERY, D. C. (2013). Design and Analysis of Experiments, Eight Ed., A John Wiley & Sons, Inc., USA.
• MODIS, T. (2013). Long-term GDP Forecast and the Prospects For Growth, Technological Forecasting & Social Change, 80(8), 1557-1562.
• NDORICIMPA, A. (2020). Threshold effects of public debt on economic growth in Africa: a new evidence. Journal of Economics and Development 22(2), 187-207.
• STOCK, J.H. and WATSON, M. W. (2002). Macroeconomic Forecasting Using Diffusion Indexes, Journal of Business and Economic Statistics, 20(2), 147-162.
• SMETS, F. and WOUTERS, R. (2003). An Estimated Dynamic Stochastic General equilibrium Model of the Euro Area, Journal of the European Economic Association, 1(5), 1123-1175.