Modeling and forecasting Botswana's Growth Domestic Product (GDP) per capita

NYONI, THABANI and MUCHINGAMI, LOVEMORE (2019): Modeling and forecasting Botswana's Growth Domestic Product (GDP) per capita.

Abstract

Using annual time series data on GDP per capita in Botswana from 1960 to 2017, the study analyzes GDP per capita using the Box – Jenkins ARIMA methodology. The diagnostic tests such as the ADF tests show that Botswana GDP per capita data is I (1). Based on the AIC, the study presents the ARIMA (3, 2, 3) model. The diagnostic tests further show that the presented model is not only stable but also suitable. The results of the study indicate that living standards in Botswana will definitely continue to improve over the next decade. Indeed, Botswana’s success story is a reality. The study offers 4 policy recommendations in an effort to help policy makers in Botswana on how to promote and maintain the much needed better living standards for all Batswana.

• Item Type: MPRA Paper
• Language: English
• Keywords: Botswana; forecasting; GDP per capita
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications; O - Economic Development, Innovation, Technological Change, and Growth > O4 - Economic Growth and Aggregate Productivity > O47 - Empirical Studies of Economic Growth ; Aggregate Productivity ; Cross-Country Output Convergence

References

1. Adebayo, A. G (2016). Forecasting the Gross Domestic Product in Nigeria using time series analysis and a sample frame of 1960 – 2015, Journal of Business and African Economy, 2 (2): 1 – 14.

2. Asteriou, D. & Hall, S. G. (2007). Applied Econometrics: a modern approach, Revised Edition, Palgrave MacMillan, New York.

3. 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.

4. Du Preez, J. & Witt, S. F. (2003). Univariate and multivariate time series forecasting: An application to tourism demand, International Journal of Forecasting, 19: 435 – 451.

5. Goh, C. & Law, R. (2002). Modeling and forecasting tourism demand for arrivals with stochastic non-stationary seasonality and intervention, Tourism Management, 23: 499 – 510.

6. Honde, G. J & Abraha, F. G (2015). Botswana Economic Outlook – Botswana, AfDB.

7. International Monetary Fund (2017). Botswana Staff Report for the 2017 Article IV consultation, IMF, Washington DC.

8. Junoh, M. Z. H. J (2004). Predicting GDP growth in Malaysia using knowledge-based indicators: a comparison between Neural Network and econometric approaches, Sunway College Journal, 1: 39 – 50.

9. Lu, Y (2009). Modeling and forecasting China’s GDP data with time series models, Department of Economics and Society, Hogskolan Dalarna.

10. Maipose, G.S and Matsheka, T.C (2009). The Indigenous Development State and Growth in Botswana. Chapter 5. Volume 2: 01-66

11. Nyoni, T & Bonga, W. G (2017). An Empirical Analysis of the Determinants of Private Investment in Zimbabwe, DRJ – Journal of Economics and Finance, 2 (4): 38 – 54.

12. Nyoni, T & Bonga, W. G (2018). What Determines Economic Growth In Nigeria? DRJ – Journal of Business and Management, 1 (1): 37 – 47.

13. Nyoni, T (2018). Modeling and Forecasting Inflation in Kenya: Recent Insights from ARIMA and GARCH analysis, Dimorian Review, 5 (6): 16 – 40.

14. Nyoni, T. (2018). Box – Jenkins ARIMA Approach to Predicting net FDI inflows in Zimbabwe, Munich University Library – Munich Personal RePEc Archive (MPRA), Paper No. 87737.

15. Okasha, M.K and Yaseen, A.A (2013). Comparison Between Arima Models And Artificial Neural Networks In Forecasting Al-Quds Indices Of Palestine Stock Exchange Market. Conference paper. Research Gate.

16. Song, H., Witt, S. F. & Jensen, T. C. (2003b). Tourism forecasting: accuracy of alternative econometric models, International Journal of Forecasting, 19: 123 – 141.

17. Wabomba, M. S., Mutwiri, M. P., & Mungai, F (2016). Modeling and forecasting Kenyan GDP using Autoregressive Integrated Moving Average (ARIMA) models, Science Publishing Group – Science Journal of Applied Mathematics and Statistics, 4 (2): 64 – 73.