Estimation of CPI of Some Sub-sahara African Countries: Panel Data Approach
American Journal of Theoretical and Applied Statistics
Volume 8, Issue 6, November 2019, Pages: 246-252
Received: Jul. 13, 2019;
Accepted: Oct. 31, 2019;
Published: Nov. 15, 2019
Views 34 Downloads 15
Oyekunle Janet Olufunmike, Department of Statistics, Federal Polytechnic, Ede, Nigeria
Ayoola Joshua Femi, Department of Statistics, University of Ibadan, Ibadan, Nigeria
Oyenuga Iyabode Favour, Department of Mathematics and Statistics, the Polytechnic, Ibadan, Nigeria
Masopa Adekunle Nurudeen, Department of Statistics, Federal Polytechnic, Ede, Nigeria
Adesiyan Adefowope Abdul Azeez, Department of Statistics, Federal Polytechnic, Ede, Nigeria
Follow on us
One of the economic indicators that are necessary to provide information on the state and progress of country is the Consumer Price Index (CPI) which measures changes in the price of goods and services over a certain period of time. An effective monetary policy depends on the ability of economists to develop a reliable model that could understand the ongoing economic processes and predict future developments. Hence, this study is aimed at estimating CPI (a component of Inflation) in 20 Sub–Sahara African (SSA) countries in relation to Broad Money (BM), Export Rate (EXP), Gross Domestic Product (GDP) and Private Consumption Expenditure (PCE) using panel data approach. The data was extracted from the World Bank Data Bank for a period of 30 years (1987-2016). The Fixed Effect Model (FEM) was employed and the model summary was computed using the panel least squares. The Variance Inflation Factor (VIF) was used to test for the presence of multicollinearity. The result of the analysis shows that the CPI for SSA countries ranges from 0.0007% to 298.51% (2010=100) with an average of 59.76%. All the predictors included in estimating the CPI have significant effect at 5% level except the GDP. The estimated panel regression equation is CPIit=71.4449-0.1735BMit-0.3309EXPit+7.4338e-12GDPit+1.1335e-10PCEit. The estimated coefficient of determination is 0.853 which means that 85.3% of the total variation in CPI can be accounted for by the variations in the macroeconomic variables included. The VIF for all the variables is less than 3.o meaning that there is no sign of multicollinearity and therefore, there is no correlation among the predictors. It was concluded that the FEM estimated can be used to assess the behavior of the CPI in the nearest future. Moreover, 85.3% of the variations in CPI can be explained by the economic variables used as independent variables. It is recommended that efforts should be geared towards improving the input of these variables in the economy such that appropriate relationship will exist between them and the CPI in the SSA nations.
CPI, SSA, PCE, EXP, GDP
To cite this article
Oyekunle Janet Olufunmike,
Ayoola Joshua Femi,
Oyenuga Iyabode Favour,
Masopa Adekunle Nurudeen,
Adesiyan Adefowope Abdul Azeez,
Estimation of CPI of Some Sub-sahara African Countries: Panel Data Approach, American Journal of Theoretical and Applied Statistics.
Vol. 8, No. 6,
2019, pp. 246-252.
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Akpanta, A. C. and Okoric, I. E (2015); on Time Series Analysis of Consumer Price Index data of Nigeria – 1996 to 2013. American Journal of Economics 2015, 5 (3): 363–369.
Umeora, Chinweobo Emmanuel (2010); Effects of Money Supply and Exchange Rates on Inflation in Nigeria. Journal of Management and Corporate Governance, Vol. 2, December, 2010.
Nguyen, H. M., Cavoli, and Wilson, J. K “The determinants of inflation in Vietnam, 2001 – 2009,” Asean Economic Bulletin, vol. 29, no. 1, pp. 1, 2012.
Saravanan Venkadasalam (2015); The Determinant of Consumer Price Index in Malaysia. Journal of Economic, Business and Management, Vol 3, No. 12.
Gungor Turan and Jona Hoxhaj (2015) Money Supply and Prices Relation in Albanian Economy. Academic Journal of Interdisciplinary studies MCSER Publishing. Rome – Italy vol. 4 NO 351.
Saeed, A. (2007) Inflation and Economic Growth in Kuwait: 1985 – 2005 Evidence from Co – integration Error – correction Model. Applied Econometrics and International Development vol. 7–1.
Sweidan O. D. (2004) Does Inflation harm Economic Growth in Jordan? An Econometric Analysis for the period 1970 – 2000 International Journal of Applied econometrics and Quantitative Studies. Vol. 1–2 pp 41–66.
Shitundu, J. L and Luvanda, E. G. (2000). The Effect of Inflaiton on Economic growth in Tanzania. African Journal of Finance and Management vol. 19–1.
Omoke Philip C. (2010) Inflation and Economic Growth in Nigeria. Journal of Sustainable Development. Vol. 3 NO 2 pp 166.
Anarfo, E. B., Abor, J. Y, Osei, K. A and Gyeke-Dako, A (2019). Monetary policy and financial inclusion in Sub-Sahara Africa using a panel VAR Approach Journal of African Business. 20 (1): 1-8.
Anh D. M, Nguyen, Jemma Dridi, Filiz D. Unsal and oral H. Williams (2015) On the Drivers of Inflation in Sub – Saharan Africa. IMF Working Paper WP/15/189.
Baltagi B. H. and Q. Li (1992); Prediction in the one-way error component model with serial correlation. Journal of Forecasting 11, 561-567.
Baltagi, B. H. (2002); Econometric Analysis of Panel Data. 2nd Edition, New York: John Wiley and Sons.
Bloomberg view.com/articles/ 2015 – 11 – 10/ sub – Saharan Africa thrills.
Hsiao, C. (2003); Analysis of Panel Data. Cambridge University Press, Cambridge.