Forecasting GDP using ARIMA and a dynamic forecast model - Empirical evidence from Libya
DOI:
https://doi.org/10.54172/jxpevq17Keywords:
Forecasting, Box - Jenkins, Dynamic Forecasting Model, GDP, LibyaAbstract
This study aims to modeling and forecasting Nominal GDP of Libya over the period 1962-2019 using Box - Jenkins Methodology and Dynamic Forecasting Model. The study examines a number of ARIMA family models and recommends ARIMA(3,1,4) as the most appropriate model that best describes the annual GDP series of the sampled period. Using ARIMA(3,1,4) and Dynamic Forecasting Model, and estimates international monetary fund the study predicts a decline of 50% and 51% and 50% respectively. Whereas, according to the ARIMA model, the study predicts continued decline in the value of the GDP for the year 2021, and an increase in the predicts value of the GDP for the year 2022.References
Hussein, Sanad. (2018). The impact of changes in money supply on GDP, exchange rate and inflation during the period 1980-2017, Master's thesis, Derna University, State of Libya.
Darwish, Marwan. (2018). "Using the Box-Jenkins methodology to predict cash flows in Palestinian banks: A case study of Palestine Bank." Al-Quds Open University Journal for Administrative and Economic Research, 3(9): 151-164.
Al-Samarrai and Al-Naimi. (2018). "Comparison of statistical methods used to predict the volume of GDP for the public and private sectors for the period (2016-2025)." Journal of Economic and Administrative Sciences, 24(107): 590-613.
Aboud, Al-Mashaat Al-Sadiq. (2018). "Using ARIMA models in forecasting inflation in the Libyan economy for the period (2016-2025)." Journal of Economic Horizons, 8(4): 1-25.
Antarah, Brash. (2018). "Forecasting inflation rates in Algeria in light of the decline in oil prices for the period 2018-2027." Annals of the University of Algiers, 32(2): 490-508.
Fandel, Walter. (1992). Time Series and Box-Jenkins Models - Translated by Abdul Mardi Hamid Azzam, Dar Al-Marikh for Publishing, Riyadh, Saudi Arabia.
Awel, Y. M. (2018). "Forecasting GDP Growth: Application of Autoregressive Integrated Moving Average Model." Empirical Economic Review 1(2): 1-16.
Dritsaki, C. (2015). "Forecasting Real GDP Rate through Econometric Models: An Empirical Study from Greece." Journal of International Business and Economics 3(1): 13-19.
Ma, L. Hu, C. Lin, R. Han, Y. (2018). "ARIMA model forecast based on EViews software." IOP Conf. Series: Earth and Environmental Science 208(012017): 1-8.
Rana, S. B. (2019). "Forecasting GDP Movements in Nepal Using Autoregressive Integrated Moving Average (ARIMA) Modelling Process." Journal of Business and Social Sciences Research 4(2): 1-20.
ZAKAI, M. (2014). "A TIME SERIES MODELING ON GDP OF PAKISTAN." Journal of Contemporary Issues in Business Research 3(4): 200-210.
Downloads
Published
Issue
Section
License
Copyright (c) 2020 علي محمد سالم الهبري، سند حسين محمد جرهان (مؤلف)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright of the articles Published by Al-Mukhtar Journal of Economic Sciences (Mjes) is retained by the author(s), who grant Mjes a license to publish the article. Authors also grant any third party the right to use the article freely as long as its integrity is maintained and its original authors and cite Mjes as the original publisher. Also, they accept the article remains published by the Mjes website (except in the occasion of a retraction of the article). All Mjes Published articles under the Attribution-NonCommercial 4.0 International License.
