Please enter verification code
Modeling and Forecasting Consumer Price Index (Case of Rwanda)
American Journal of Theoretical and Applied Statistics
Volume 5, Issue 3, May 2016, Pages: 101-107
Received: Apr. 10, 2016; Accepted: Apr. 20, 2016; Published: May 3, 2016
Views 6790      Downloads 362
Habimana Norbert, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Anthony Wanjoya, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Anthony Waititu, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Article Tools
Follow on us
Consumer price index is a measure of the average change over time in the price of consumer items, goods and services that households buy for day to day living. It is one of the main indicators of economic performance and also the key indicator of the results of the monetary policy of the country, because of its wide use as a measure of inflation. The main objective of this research was to model the dynamic of CPI and to forecast its future values in the short term. Therefore, to come up with a model and forecasts of CPI, Box and Jenkins methodology were used which consists of three main steps; Model Identification, Parameter Estimation and Diagnostic Checking. Therefore, ARIMA (4,1,6) was selected as a potential model which can fits well data, as well as to make also accurate forecast. Hence, the forecast was made for 12 months ahead of the year 2016, and the findings have shown that the CPI was likely to continue rising up with time.
Consumer Price Index, ACF, PACF, ARIMA Model
To cite this article
Habimana Norbert, Anthony Wanjoya, Anthony Waititu, Modeling and Forecasting Consumer Price Index (Case of Rwanda), American Journal of Theoretical and Applied Statistics. Vol. 5, No. 3, 2016, pp. 101-107. doi: 10.11648/j.ajtas.20160503.14
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
National Bank of Rwanda (2015), Consumer Price Index. Statistical bulletin.
Pufnik, A. and D. Kunovac (2006). Short-Term Forecasting of Inflation in Croatia with Seasonal ARIMA Processes. Working Paper, W-16, Croatia National Bank.
Mordi, C. N. O, Adeby, M. A, and Adamgbe, E. T (2012). Short-term inflation forecasting for monetary policy in Nigeria, central Bank of Nigeria Occasion Paper No. 42
Asel ISAKOVA (2007). Modeling and Forecasting inflation in developing Countries: The case of Economies in Central Asia. Discussion Paper No. 2007-174
Prapanana M, Labani S. and Saptarsi G. (2014). Study of Effectiveness of Time series Modeling (ARIMA) in Forecasting Stock Prices. Internationa Journal of computer Sciences, Engineering and Application. Vol. 4, No. 2
Jiban Chandra P., Md Shahidul Hoque and Mohammad M. Rahman (2013). Selection of Best ARIMA Model for forecasting Average Dairly Share Price Index of pharmaceutical Companies in bangladesh: A case Study on Squaree pharmaceutical Ltd. Global Journal Inc. (USA), Volume 13
Ayodele A. Adebiyi, Aderemi O. Adewumi and Charles K. Ayo (2014). Stock Price Prediction Using ARIMA Model. International conference on Computer Modelling and simulation. No 16
Brockwell, P. J and Davis, A. R. (2002). Introduction to time Series and forecasting, Second Edition, Springer Verlag.
Fuller, W. A. (1996). Introduction to Statistical Time Series. John Wiley & Sons.
Ngai hang C. (2002). Time series Application to Finance. John Wiley & Sons.
S. O. Adams, A. Awujola, A. I. Alumgudu (2014). Modeling Nigeria’s Consumer Price index using ARIMA model. International Journal of Development and Economic Sustainability. Vol. 2, No. 2, pp. 37-47, June 2014.
Suleman, N. and S. Sarpong (2012). Empirical Approach to Modeling and Forecasting Inflation in Ghana. Current Research Journal of Economic Theory Vol. 4, no 3: 83-87.
Sani I. Dogua and Sarah O. Alade (2013). Short-Term Inflation Forecasting for Nigeria. CBN\ Journal of Applied Statistics Vol. 4 No.2
Tahsina Akhter (2013). Short-Term Forecasting of Inflation in Bangladesh with Seasonal ARIMA Processes. Munich Personal RePEc Archive (MPRA) Paper No. 43729.
Abraham and Katharine G., (1997). The CPI commission: Discussion, American Economic Review, May 1997.
[16] Consulted on 25 june 2015.
National Institute of Statistics of Rwanda (2015). CPI Publication.
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
Tel: (001)347-983-5186