Modelling and Forecasting Somalia GDP Using Autoregressive Integrated Moving Average (ARIMA) Models

Sadak Mohamud Hassan

doi:10.37284/eajbe.6.1.1356

Sadak Mohamud Hassan Ondokuz Mayis University

DOI: https://doi.org/10.37284/eajbe.6.1.1356

Keywords: Box-Jenkins Approach, Somalia, Forecasting, Goodness-of-fit Measures, Gross Domestic Product, Residuals Analysis

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Abstract

The Gross Domestic Product (GDP) is the total worth of all goods and services produced within a country's borders in a given year. The background of the study includes the importance of GDP as an important economic indicator reflecting the overall economic performance and growth of a country. As Somalia faces unique economic challenges, this research aims to provide insight into its GDP dynamics, trends, and potential future developments. In order to create the suitable Autoregressive-Integrated Moving-Average (ARIMA) model for the GDP data for Somalia, the Box-Jenkins method was used in this study. Data on the annual GDP of Somalia from 1972 through 2022 was taken from the Macrotrends database. ARIMA (3, 1, 8) was identified as the most suitable statistical model for the GDP of Somalia. The forecast for Somalia's GDP over the next five years was generated using this fitted ARIMA model. The findings indicated that a positive increase in Somalia's GDP over the next five years is expected. These forecasts line up with the historical trends and statistical correlations found by the ARIMA model

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References

Abonazel, M. R., & Abd-Elftah, A. I. (2019). Forecasting Egyptian GDP using ARIMA models. Reports on Economics and Finance, 5(1), 35-47.

Agrawal, V. (2018). GDP modelling and forecasting using ARIMA: an empirical study from India. Central European University.

Box, G. (2013). Box and Jenkins: time series analysis, forecasting and control. In A Very British Affair: Six Britons and the Development of Time Series Analysis During the 20th Century (pp. 161-215). London: Palgrave Macmillan UK.

Chatfield, C., & Xing, H. (2019). The analysis of time series: an introduction with R. CRC Press.

Frain, J. (1992). Lecture notes on univariate time series analysis and box jenkins forecasting. Economic Analysis, Research and Publications, 189-194.

Ghazo, A. (2021). Applying the ARIMA Model to the Process of Forecasting GDP and CPI in the Jordanian Economy. International Journal of Financial Research, 12(3), 70.

Granger, C. W. J., & Newbold, P. (2014). Forecasting economic time series. Academic press.

Kiriakidis, M., & Kargas, A. (2013). Greek GDP forecast estimates. Applied Economics Letters, 20(8), 767-772.

Osberg, L., & Sharpe, A. (2002). An index of economic well–being for selected OECD countries. Review of Income and Wealth, 48(3), 291-316.

Shahini, L., & Haderi, S. (2013). Short term Albanıan GDP Forecast:" One quarter to one year ahead". European Scientific Journal, 9(34).

Young, W. L. (1977). The Box-Jenkins approach to time series analysis and forecasting: principles and applications. RAIRO-Operations Research-Recherche Opérationnelle, 11(2), 129-143.

Zhang, H., & Rudholm, N. (2013). Modelling and forecasting regional GDP in Sweden using autoregressive models. Dalama University.