Forecasting Inflation Rates in The Libyan Economy Using Exponential Smoothing Methods
Keywords:
Forecasting, Inflation, Libyan economy, exponential smoothing, CPI
Abstract
The main aim of this study was to forecast inflation rates in the Libyan economy during the period 2020-2025. In order to achieve its objectives, the study has used CPI (2003=100) as an index of inflation in Libya, and adopted exponential smoothing methods. The main findings of the study have indicated that HW-no seasonality method is the most appropriate method for forecasting inflation rates in Libya. The forecasting results has showed that inflation rates in Libya will sharply decline in 2020. However, it will increase along the rest of the period.
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References
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النشرة الاقتصادية. (2019). طرابلس: مصرف ليبيا المركزي.
عبد المجيد بو ساق، و فريد برارة. (2020). التنبؤ بمعدلات التضخم الفصلية باستخدام السلاسل الزمنية دراسة حالة الجزائر (1980-2018). مجلة آفاق علمية، 12(3)، 417-437.
علي عطية عبد السلام. (1995). الجذور النقدية للتضخم. ندوة ظاهرة التضخم في الاقتصاد الليبي. طرابلس: جمعية الاقتصاديين الليبيين.
عنترة برباش. (2018). التنبؤ بمعدلات التضخم في الجزائر في ظل انخفاض أسعار البترول للفترة 2018-2027. 32(1،2)، 490-508.
فتحي أبو سدرة. (1995). التخصيصية والتضخم في الاقتصاد الليبي. ندوة ظاهرة التضخم في الاقتصاد الليبي. طرابلس: جمعية الاقتصاديين الليبيين.
Abdelmadjid, Bousak & Farid, Brara. (2020). To predict seasonal inflation rates using time series, Algeria case study (1980-2018). Afak Ilmiya Journal, 12(3). 417-437.
Ali, A. Salem. (1995). The monetary roots of inflation. Symposium on the phenomenon of inflation in the Libyan economy. Tripoli: Association of Libyan Economists.
Antara, Beryache. (2018). Forecasting inflation rates in Algeria in light of the decline in oil prices for the period 2018-2027. 32 (1,2), 490-508.
Fathi, Aboucedra. (1995). Privatization and inflation in the Libyan economy. Symposium on the phenomenon of inflation in the Libyan economy. Tripoli: Association of Libyan Economists.
Barro, R. J. (2013). Inflation and economic growth. Annals of Economics & Finance, 14(1).
Bilel, A. Hassen, T. Fakhri, I & Habib, Z. (2015). Forecasting Inflation in Tunisia into instability: Using Dynamic Factors Model a two-step based on Kalman filtering. MPRA Paper No. 68455, Online at https://mpra.ub.uni-muenchen.de/68455/
Bruneau, C. De Bandt, O. Flageollet, A & Emmanuel Michaux. (2005). forecasting inflation using eonomic indicators: the case of France. Banque de France, http://www.banque-france.fr/
Darrat, A. F., & Arize, A. C. (1990). Domestic and international sources of inflation in developing countries: Some evidence from the monetary approach. International Economic Journal, 4(4), 55-69.
Huselius, E., & Walled, L. (2014). Forecasting inflation in Sweden: A univariate approach. Stockholms Universitet, Department of Statistics, (2014), 11.
Jere, S., & Siyanga, M. (2016). Forecasting inflation rate of Zambia using Holt’s exponential smoothing. Open journal of Statistics, 6(2), 363-372.
Lidiema, C. (2017). Modelling and Forecasting Inflation Rate in Kenya Using SARIMA and Holt-Winters Triple Exponential Smoothing. American Journal of Theoretical and Applied Statistics, 6(3), 161-169.
Moriyama, K., & Naseer, A. (2009). Forecasting inflation in Sudan (No. 9-132). International Monetary Fund.
Neusser, K. (2019) Time Serie Econometrics. Springer International Publishing AG Switzerland.
Nyoni, T. (2018). modeling and forecasting inflation in Kenya: recent insights from ARIMA and GARCH analysis. Dimorian Review, 5(6) 16-40.
Sekine, T. (2001). Modeling and Forecasting Inflation in Japan. (No. 01-82). International Monetary Fund.
Published
2021-01-27
How to Cite
Hussen Faraj. (2021). Forecasting Inflation Rates in The Libyan Economy Using Exponential Smoothing Methods. Dirassat Journal Economic Issue, 12(1), 1-14. https://doi.org/10.34118/djei.v12i1.1114
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Copyright (c) 2021 Hussen Faraj
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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