Comparison of The Winters’ Seasonality Exponential Smoothing Method With The Pegels’ Classification: Forecasting of Turkey's Economic Growth Rates

Yıl 2019, Cilt: 19 Sayı: 3, 275 - 294, 30.09.2019

İbrahim Orkun Oral

https://doi.org/10.18037/ausbd.632023

Cited By: 2

Öz

Being one of the macroeconomic indicators, economic growth is a significant indicator, which shows development level of countries and welfare level of people living within the border of a country. Economic growth has a great importance especially for policy makers. Therefore, forecasting economic growth of a country is of vital importance in taking decisions such as long-term investment, employment etc. and developing, regulating and revising the policies of countries. The study aims to compare the selected exponential smoothing methods used in forecasting of Turkey’s economic growth indicators and determine the most appropriate technique. To this end, economic growth rate of Turkey between 1998 and the second quarter of 2018 was addressed and economic growth rate for the third and fourth quarters of 2018 was forecasted depending on the economic growth rate in the second quarter of 2018. Forecasts were carried out by using Winters’ seasonality exponential smoothing method based on the characteristics of time series and model selection criteria and additive Holt-Winters’ seasonality exponential smoothing method in the Cell B-2 of Pegels’ classification. It has been found out that the most appropriate method for the relevant forecasts is the additive Holt-Winters’ seasonality exponential smoothing method. It has been concluded that there would be 11,995% increase in the third quarter and 6,415% increase in the fourth quarter depending on the economic growth rate in the second quarter of 2018.

Anahtar Kelimeler

Economic Growth, Forecast, Exponential Smoothing Methods, Economic Growth Forecast

Kaynakça

• Aghion P. ve Howitt P. (1998). Endogenous Growth Theory. England: The MIT.
• Arrow, K.J. (1962). The Economic Implication of Learning by Doing. Review of Economic Studies, 29, 155-173.
• Atamtürk, B. (2007). Büyüme Teorileri ve IMF Politikaları. Marmara Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 22(1), 89-103.
• Becker, G.S., Murphy, K. M. ve Tamura, R. (1990). Human Capital, Fertility and Economic Growth. Journal of Political Economy, 98(5), 12-37.
• Berber, M., (2007). İktisadi Büyüme ve Kalkınma, Trabzon: Derya,.
• Bergmeir, C., Hyndman, R. J. ve Benitez, J. M. (2016). Bagging Exponential Smoothing Methods Using STL Decomposition and Box–Cox Transformation. International Journal of Forecasting, 32, 303-312.
• Braimllari, A. ve Sala, E. (2016). Modeling and Forecasting of Food Imports in Albania. The AlbanianJournal of AgriculturalSciences, 15(4), 200-205.
• Dong, Z. ve Zhu, G. S. (2014). A Modified Exponential Smoothing Model for Forecasting Per Capita GDP in Yunnan Minority Area. Applied Mechanics and Materials, 599(601), 2074-2078.
• Dritsaki, C. (2015). Forecasting Real GDP Rate through Econometric Models: An Empirical Study from Greece. Journal of International Business and Economics, 3(1), 13-19. Goodwin, P. (2010). The Holt-Winters Approach to Exponential Smoothing: 50 Years Old and Going Strong. Foresight: The International Journal of Applied Forecasting, 19, 30-33.
• Grossman,G.M. ve Helpman, E. (1991). Quality Ladders in the Theory of Growth. Review of Economic Studies, 58(1), 43-61.
• Grossman,G.M. ve Helpman, E. (1994). Endogenous Innovation in the Theory of Growth. Journal of Economic Perspectives, 8(1), 21-44.
• Hussain, A. ve Nazir, N. (2013). Analysis of Growth Rates in Different Regimes of Pakistan: Distribution and Forecasting. Journal of Managerial Sciences, 7(1), 37-57.
• Hyndman, R. J. ve Koehler, A. B. (2006). Another Look at Measures of Forecast Accuracy. International Journal of Forecasting, 22, 679-688.
• Junoh, M. (2004). Predicting GDP Growth in Malaysia Using Knowledge-Based Economy Indicators: A Comparison Between Neural Network And Econometric Approaches. Sunway College Journal, 1, 39-50.
• Li, M., Liu, G. ve Zhao, Y. (2007). Forecasting GDP Growth Using Genetic Programming. Prooceedings of Third International Conference on Natural Computation (ICNC 2007), 24-27 Ağustos 2007, Haikou, Çin, 393-397
• Lucas, R. (1988). On The Mechanics of Economic Development. Journal of Monetary Economics. 22, 3-42.
• Makridakis, S., ve Hibon, M. (2000). The M3-Competition: Results, Conclusions and Implications. . International Journal of Forecasting, 16, 451-476.
• Makridakis, S., Wheelwright, S. C. ve Hyndman, R. J. (1998). Forecasting: Methods And Applications. John Wiley & Sons. Inc, United State of America.
• Mankiw, G. N., Romer, D. ve Weil, D. N. (1992). A Contribution to the Empirics of Economic Growth. The Quarterly Journal of Economics, 107(2), 407-437.
• Mirbagheri, M. (2010). Fuzzy-Logic and Neural Network Fuzzy Forecasting of Iran GDP Growth. African Journal of Business Management, 4(6), 925-929.
• Montgomery, D. C., Jennings, C. L. ve Kulahci, M. (2015). Introduction to Time Series Analysis and Forecasting. John Wiley & Sons, Inc, Hoboken.
• Parasız, İ. (2008). Ekonomik Büyüme Teorileri., Bursa: Ezgi.
• Pegels, C. C. (1969). Exponential Forecasting: Some New Variations. Management Science, 15(5), 311 – 315.
• Rebelo, S. (1991). Long-Run Policiy Analysis and Long-Run Growth. Journal of Political Economy, 99 (3), 500-521.
• Romer, M. P. (1994). The Origins of Endegenous Growth. Journal of Economic Perspectives, 8(1), 3-22.
• Romer, P. (1986). Increasing Returns and Long-Run Growth. Journal of Political Economy, 94, 1002-1037.
• Samimi, A. J., Shirazi, B. ve Fazlollahtabar, H. (2007). A Comparison Between Time Series, Exponential Smoothing, and Neural Network Methods to Forecast GDP of Iran. Iranian Economic Review, 12(19), 19-35.
• Solow, R. M. (1994). Perspectives on Growth Theory. Journal of Economic Perspectives, 8(1), 45-54.
• Söyler, H. ve Kızılkaya, O. (2015). Türkiye’nin GSYİH Tahmini için Yapay Sinir Ağları Model Performanslarının Karşılaştırılması. Celal Bayar Üniversitesi İktisadi ve İdari Bilimler Dergisi, 16(1), 45-58.
• Temuçin, T. ve Temiz, İ. (2016). Türkiye Dış Ticaret İhracat Hacminin Projeksiyonu: Holt-Winters ve Box, Jenkins Modellerinin Bir Kıyaslaması. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 21 (3), 937-960.
• Tüzemen, A. ve Yıldız, Ç. (2018). Holt-Winters Tahminleme Yöntemlerinin Karşılaştırmalı Analizi: Türkiye İşsizlik Oranları Uygulaması. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 32(1), 1-18.
• Ünsal, A. (1997). Zaman Serilerinde Regresyon ve Varyans Analizi Yöntemleri İle Mevsimsel Dalgalanmaların Araştırılması ve Bir Uygulama. Ekonomik Yaklaşım, 8 (26), pp. 119 – 130.
• Üzümcü, A. (2015). İktisadi Büyüme. İstanbul: Beta.
• Valakevicius, E. ve Brazenas, M. (2015). Application of the Seasonal Holt-Winters Model to Study Exchange Rate Volatility. Inzinerine Ekonomika-Engineering Economics, 26 (4), 384-390.
• Winters, P. R. (1960). Forecasting Sales By Exponentially Weighted Moving Averages. Managment Science, 6(3), 324 – 342.
• Yıldırım, H. ve Başeğmez, H. (2017). Analysis and Forecast of Turkey Unemployment Rate. Global Journal of Mathematical Analysis, 5(1), 11-15.
• Young, A. (1991). Learning by Doing and the Dynamic Effect of International Trade. The Quarterly Journal of Economics, 106(2), 369-405.
• Zakai, M. (2014). A Time Series Modeling on GDP of Pakistan. Journal of Contemporary Issues in Business Research, 3(4), 200-2