American Journal of Applied Mathematics and Statistics
ISSN (Print): 2328-7306 ISSN (Online): 2328-7292 Website: http://www.sciepub.com/journal/ajams Editor-in-chief: Mohamed Seddeek
Open Access
Journal Browser
Go
American Journal of Applied Mathematics and Statistics. 2014, 2(1), 16-28
DOI: 10.12691/ajams-2-1-4
Open AccessArticle

Application of Sarima Models in Modelling and Forecasting Nigeria’s Inflation Rates

Otu Archibong Otu1, Osuji George A.2, Opara Jude3, , Mbachu Hope Ifeyinwa3 and Iheagwara Andrew I.4

1Department of Statistics, Central Bank of Nigeria, Owerri

2Department of Statistics, Nnamdi Azikiwe University, PMB 5025, Awka Anambra State Nigeria

3Department of Statistics, Imo State University, PMB 2000, Owerri Nigeria

4Department of Planning, Research and Statistics, Ministry of Petroleum and Environment Owerri Imo State Nigeria

Pub. Date: January 06, 2013

Cite this paper:
Otu Archibong Otu, Osuji George A., Opara Jude, Mbachu Hope Ifeyinwa and Iheagwara Andrew I.. Application of Sarima Models in Modelling and Forecasting Nigeria’s Inflation Rates. American Journal of Applied Mathematics and Statistics. 2014; 2(1):16-28. doi: 10.12691/ajams-2-1-4

Abstract

This paper discussed the Application of SARIMA Models in Modeling and Forecasting Nigeria’s Inflation Rates. Time series analysis and forecasting is an efficient versatile tool in diverse applications such as in economics and finance, hydrology and environmental management fields just to mention a few. Among the most effective approaches for analyzing time series data, the method propounded by Box and Jenkins, the Autoregressive Integrated Moving Average (ARIMA) was employed in this study. In this paper, we used Box-Jenkins methodology to build ARIMA model for ’s monthly inflation rates for the period November 2003 to October 2013 with a total of 120 data points. In this research, ARIMA (1, 1, 1) (0, 0, 1)12 model was developed, and obtained as = 0.3587yt+0.6413yt-1-0.8840et-11 -0.7308912et-12+0.8268et. This model is used to forecast ’s monthly inflation for the upcoming year 2014. The forecasted results will help policy makers gain insight into more appropriate economic and monetary policy in other to combat the predicted rise in inflation rates beginning the first quarter of 2014.

Keywords:
ARIMA Model SARIMA Model forecasting ARMA Model Box-Jenkins Methods inflation rates Akaike Information Criteria Bayesian Information Criterion

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Figures

Figure of 16

References:

[1]  Rudiger Dornbusch and Stanley Fischer, (1993). “Moderates Inflation”, The Bank Economic Review, Vol.7, Issue 1, Pp.1-44.
 
[2]  Jack H.R, Bond M.T., and Webb J.R, (1989). “The Inflation- Hedging Effectiveness of Real Estate”. Journal of Real Estate Research, Vol.4. Pp. 45-56.
 
[3]  Hamidreza M. and Leila S. (2012). Using SARFIMA model to study and predict the Iran’s oil supply. International Journal of Energy Economics and Policy. Vol.2, No.1, 2012, pp.41-49.
 
[4]  Fritzer, F., Gabriel, M. and Johann, S. (2002). "Forecasting Austrian HICP and its Components using VAR and ARIMA Models," Working Papers 73, Oesterreichische National bank (Austrian Central Bank).
 
[5]  Gomez V., and Maravall A., (1998.) "Automatic Modelling Methods for Univariate Series," Banco de España Working Papers 9808, Banco de España.
 
[6]  Leila S. and Masoud Y. (2012). An Empirical Study of the Usefulness of SARFIMA models in Energy Science. International Journal of Energy Science. IJES Vol.2 No.2 2012.
 
[7]  [Jeffrey J., (1990). “Business forecasting Methods”. Atlantic Publishers.
 
[8]  Box, G. E. P and Jenkins, G.M., (1976). “Time series analysis: „Forecasting and control,” Holden-Day, San Francisco.
 
[9]  Akaike, H. (1974). A New Look at the Statistical Model Identification. IEEE Transactions on Automatic Control 19 (6): 716-723.