Modelling GDP for Sudan using ARIMA

Moahmed Hassan, Hisham and Haleeb, Amin (2020): Modelling GDP for Sudan using ARIMA.

Abstract

This paper aims to obtain an appropriate ARIMA model for the Sudan GDP using the Box- Jenkins methodology during the period 1960-2018 the various ARIMA models with different order of autoregressive and moving-average terms were compared. The appropriate model for Sudan is an ARIMA (1,1,1), the results of an in-sample forecast showed that the relative and predicted values were within the range of 5%, and the forecasting effectiveness of this model, its relatively adequate and efficient in modeling the annual GDP of the Sudan.

• Item Type: MPRA Paper
• Language: English
• Keywords: ARIMA Modelling, Box-Jenkins methodology, forecasting, GDP, Sudan.
• Subjects: E - Macroeconomics and Monetary Economics > E0 - General > E00 - General; E - Macroeconomics and Monetary Economics > E0 - General > E01 - Measurement and Data on National Income and Product Accounts and Wealth ; Environmental Accounts; E - Macroeconomics and Monetary Economics > E6 - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook > E60 - General

References

Box, G. E. P. & Jenkins G. M. (1976). Time series analysis. Forecasting and control. Holden-Day, San Francisco. C. Chatfield, (2016). The Analysis of Time Series: An Introduction. CRC Press. C. Dritsaki, (2015). Forecasting real GDP rate through econometric models: An empirical study from Greece, Journal of International Business and Economics, 3, 13-19. https://doi.org/10.15640/jibe.v3n1a2 C. W. Granger, P (1986). Newbold, Forecasting Economic Time Series, Academic Press. C. M. Jarque, A. K. Bera (1980). Efficient Tests for Normality, Homoscedasticity and Serial Independence of Regression Residuals, Economics Letters, 6 , 255-259. https://doi.org/10.1016/0165-1765(80)90024-5 Dickey, D. A. & Fuller W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root.Journal of the American Statistical Association, 74(366), 427–431. The European System of Accounts ESA 1995, Eurostat, 1996. G. Kirchgässner, J. Wolters and U. Hassler(2013). Univariate Stationary Processes, in Introduction to Modern Time Series Analysis, Springer, Berlin, Heidelberg, 27-93. https://doi.org/10.1007/978-3-642-33436-8_2 Jarque, C. M.,& Bera A. K.(1980).Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics Letters6(3), 255–259. Jovanovic, B. & Petrovska M. (2010). Forecasting Macedonian GDP: Evaluation of different models for short-term forecasting. Working Paper, National Bank of the Republic of Macedonia. Ljung, G. M., & BoxG. E. P. (1978). On a measure of a lack of fit in time series models. Biometrika,65(2), 97–303. Maity, B., & ChatterjeeB. (2012). Forecasting GDP growth rates of India: An empirical study. International Journal of Economics and Management Sciences, 1(9), 52-58. M. S. Wabomba, M. P. Mutwiri and M. Fredrick (2016). Modeling and Forecasting Kenyan GDP Using Autoregressive Integrated Moving Average (ARIMA) Models, Science Journal of Applied Mathematics and Statistics, 4, 64-73. https://doi.org/10.11648/j.sjams.20160402.18 M. N. A. Bhuiyan, K. S. Ahmed and R. Jahan (2008). Study on Modeling and Forecasting of the GDP of Manufacturing Industries in Bangladesh, Chiang Mai University Journal of Social Science and Humanities, 2, 143- 157. J. Frain, (1992). Lecture Notes on Univariate Time Series Analysis and Box Jenkins Forecasting, Economic Analysis, Research and Publications. Phillips, P. C. B. & Perron P. (1988). Testing for a unit root in time series regression. Biometrika,75(2), 335–346. Shahini, L. &Haderi S. (2013). Short term Albanian GDP forecast: One quarter to one year ahead. European Scientific Journal, 9(34),198-208. Wei N, Bian K. & Y. Zhi-fa (2010). Analysis and forecast of Shaanxi GDP based on the ARIMA model. Asian Agricultural Research, 2(1), 34-41. W. L. Young, (1977). The Box-Jenkins Approach to Time Series Analysis and Forecasting: Principles and Applications, RAIRO-Operations Research Recherche Opérationnelle, 11, 129-143. https://doi.org/10.1051/ro/1977110201291 W. Ning, B. Kuan-jiang and Y. Zhi-fa (2010). Analysis and Forecast of Shaanxi GDP Based on the ARIMA Model, Asian Agricultural Research, 2, 34-41. Zakai, M. (2014). A time series modeling on GDP of Pakistan. Journal of Contemporary Issues in Business Research,3(4), 200-210. Zhang, H. (2013). Modeling and forecasting regional GDP in Sweden using autoregressive models. Working Paper, Högskolan Dalarna University, Sweden.