Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/281585 
Year of Publication: 
2021
Citation: 
[Journal:] Amfiteatru Economic Journal [ISSN:] 2247-9104 [Volume:] 23 [Issue:] 57 [Year:] 2021 [Pages:] 517-532
Publisher: 
The Bucharest University of Economic Studies, Bucharest
Abstract: 
The purpose of this paper is to compare the accuracy of the three types of models: Autoregressive Integrated Moving Average (ARIMA) models, Holt-Winters models and Neural Network Auto-Regressive (NNAR) models in forcasting the Harmonized Index of Consumer Prices (HICP) for the countries of European Union and the Western Balkans (Montenegro, Serbia and Northern Macedonia). The models are compared based on the values of ME, RMSE, MAE, MPE, MAPE, MASE and Theil's U for the out-of-sample forecast. The key finding of this paper is that NNAR models give the most accurate forecast for the Western Balkans countries while ARIMA model gives the most accurate forecast of twelve-month inflation in EU countries. The Holt-Winters (additive and multiplicative) method proved to be the second best method in case of both group of countries. The obtained results correspond to the fact that the European Union has been implementing a policy of strict inflation targeting for a long time, so the ARIMA models give the most accurate forecast of inflation future values. In the countries of the Western Balkans the targeting policy is not implemented in the same way and the NNAR models are better for inflation forecasting.
Subjects: 
Inflation
Holt-Winters models
Autoregressive Integrated Moving Averagemodels
Neural Network Auto-regression models
forecasting
JEL: 
C51
C53
E31
E37
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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