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Samimi, A. J., Shirazi, B., & Fazlollahtabar, H. (2007). A Comparison between Time Series, Exponential Smoothing, and Neural Network Methods to Forecast GDP of Iran. Iranian Economic Review, 12, 19-35.

has been cited by the following article:

• TITLE: Comparison of the Holt-Winters Exponential Smoothing Method with ARIMA Models: Forecasting of GDP per Capita in Five Balkan Countries Members of European Union (EU) Post COVID

  AUTHORS: Melina Dritsaki, Chaido Dritsaki

  KEYWORDS: GDP per Capita, ARIMA, Holt-Winters, Zivot-Andrews, Forecasting, Balkan Countries of EU

  JOURNAL NAME: Modern Economy, Vol.12 No.12, December 30, 2021

  ABSTRACT: Gross Domestic Product (GDP) is the most frequently used measure of total product in an economy while GDP per capita is used for comparing living conditions or for watching the convergence or divergence among member countries of European Union (EU). This paper presents how two techniques can be applied to the same data set and how their performance can be evaluated and compared. We chose ARIMA model and Holt-Winters exponential smoothing method to forecast the GDP per capita of five Balkan countries-members of EU and to find the model that provides more accurate prediction. To achieve this, we apply the Root Mean Square Error (RMSE), the Mean Actual Error (MAE), the Mean Actual Percentage Error (MAPE), the Symmetric Mean Absolute Percentage Error (SMAPE) criteria and Theil’s U statistics. Based on statistical metrics ARIMA is the best forecasting model and fits performance for the examined period in four out of five countries.