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Inflation Forecasting in the Western Balkans and EU: A Comparison of Holt-Winters, ARIMA and NNAR Models

Author

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  • Vesna Karadzic

    (University of Montenegro, Podgorica, Montenegro)

  • Bojan Pejovic

    (University of Montenegro, Podgorica, Montenegro)

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.

Suggested Citation

  • Vesna Karadzic & Bojan Pejovic, 2021. "Inflation Forecasting in the Western Balkans and EU: A Comparison of Holt-Winters, ARIMA and NNAR Models," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 23(57), pages 517-517.
    • Handle: RePEc:aes:amfeco:v:23:y:2021:i:57:p:517
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    References listed on IDEAS

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    1. Roberto Golinelli & Renzo Orsi, 2002. "Modelling Inflation in EU Accession Countries: The Case of the Czech Republic, Hungary and Poland," Eastward Enlargement of the Euro-zone Working Papers wp09, Free University Berlin, Jean Monnet Centre of Excellence, revised 01 Aug 2002.
    2. Peter R. Winters, 1960. "Forecasting Sales by Exponentially Weighted Moving Averages," Management Science, INFORMS, vol. 6(3), pages 324-342, April.
    3. repec:onb:oenbwp:y::i:73:b:1 is not listed on IDEAS
    4. Andreja Pufnik & Davor Kunovac, 2006. "Short-Term Forecasting of Inflation in Croatia with Seasonal ARIMA Processes," Working Papers 16, The Croatian National Bank, Croatia.
    5. McAdam, Peter & McNelis, Paul, 2005. "Forecasting inflation with thick models and neural networks," Economic Modelling, Elsevier, vol. 22(5), pages 848-867, September.
    6. Kenny, Geoff & Meyler, Aidan & Quinn, Terry, 1998. "Forecasting Irish inflation using ARIMA models," Research Technical Papers 3/RT/98, Central Bank of Ireland.
    7. Hubrich, Kirstin, 2005. "Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy?," International Journal of Forecasting, Elsevier, vol. 21(1), pages 119-136.
    8. Guido Ascari & Argia M. Sbordone, 2014. "The Macroeconomics of Trend Inflation," Journal of Economic Literature, American Economic Association, vol. 52(3), pages 679-739, September.
    9. Holt, Charles C., 2004. "Forecasting seasonals and trends by exponentially weighted moving averages," International Journal of Forecasting, Elsevier, vol. 20(1), pages 5-10.
    10. Holt, Charles C., 2004. "Author's retrospective on 'Forecasting seasonals and trends by exponentially weighted moving averages'," International Journal of Forecasting, Elsevier, vol. 20(1), pages 11-13.
    11. Friedrich Fritzer & Gabriel Moser & Johann Scharler, 2002. "Forecasting Austrian HICP and its Components using VAR and ARIMA Models," Working Papers 73, Oesterreichische Nationalbank (Austrian Central Bank).
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Inflation; Holt-Winters models; Autoregressive Integrated Moving Average models; Neural Network Auto-regression models; forecasting;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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