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.