Is Nigeria's economy progressing or backsliding? Implications from ARIMA models

NYONI, THABANI (2019): Is Nigeria's economy progressing or backsliding? Implications from ARIMA models.

Abstract

Using annual time series data on GDP per capita in Nigeria from 1960 to 2017, I model and forecast GDP per capita using the Box – Jenkins ARIMA technique. My diagnostic tests such as the ADF tests show that Nigerian GDP per capita data is I (1). Based on the AIC, the study presents the ARIMA (2, 1, 0) model. The diagnostic tests further reveal that the presented optimal model is stable and hence reliable. The results of the study indicate that living standards in Nigeria will tumble over the next decade, as long as the current economic policy stance is not reviewed. Indeed, Nigeria’s economy is backsliding again!!! In order to improve the living standards of an ordinary Nigerian, this study has put forward four-fold policy prescriptions.

• Item Type: MPRA Paper
• Language: English
• Keywords: GDP per capita; forecasting; Nigeria
• 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] Asteriou, D. & Hall, S. G. (2007). Applied Econometrics: a modern approach, Revised Edition, Palgrave MacMillan, New York.

[2] CBN (2015). Forecasting Nigeria GDP growth rate using a dynamic factor model in a state space framework, Research Department, Central Bank of Nigeria (CBN), Abuja.

[3] 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.

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

[5] Maliki, O. S., Emmanuel, I & Obinwanne, E. E (2014). Neural network applications in the forecasting of GDP of Nigeria as a function of key stock market indicators, Pelagia Research Library – Advances in Applied Research, 5 (3): 204 – 212.

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

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

[8] Nyoni, T (2018f). The war against corruption in Nigeria: devouring or sharing the national cake? Munich University Library – Munich Personal RePEc Archive (MPRA), Paper No. 87615.

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

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

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