RESEARCH JOURNAL OF BUSINESS AND ECONOMIC MANAGEMENT
Integrity Research Journals

Model: Open Access/Peer Reviewed
DOI: 10.31248/RJBEM
Start Year: 2017
Email: rjbem@integrityresjournals.org


A predictive Autoregressive Integrated Moving Average (ARIMA) Model for forecasting inflation rates

https://doi.org/10.31248/RJBEM2018.012   |   Article Number: FC35E6851   |   Vol.1 (1) - June 2018

Received Date: 12 March 2018   |   Accepted Date: 13 June 2018  |   Published Date: 30 June 2018

Authors:  Terna A. J. , Adubisi O. D. , Awa U. E. , David I. J. and James F.E.

Keywords: ARIMA, consumer price index, forecast, inflation rates, Unit-root test.

This paper considers the modelling and forecasting of monthly All-items (12 months average change) inflation rates in Nigeria using the Box-Jenkins (ARIMA) model. Time series data used in the study was collected from the Central Bank of Nigeria statistical web database. The data was differenced twice to achieve stationarity in the series as required. Based on the evaluation and diagnostic criteria, the most accurate model is selected. The order of the best ARIMA model was found to be ARIMA (1, 2, 1). The diagnostic analysis of the model residuals showed that they are normally distributed uncorrelated random shocks. The findings in this study showed that the selected ARIMA model captured the dynamics in the series and produced forecasted values which had minimal forecast errors when compared with the actual inflation values in the validation period.

Adubisi, O. D., Eleke, C. C., Mom, T. T., & Adubisi, C. E. (2017b). Application of SARIMA to modelling and forecasting money circulation in Nigeria. Asian Research Journal of Mathematics, 6(1), 1-10.
Crossref
 
Adubisi, O. D., Mom, T. T., Adubisi, C. E., & Luka, P. (2017a). Predictive Model with Square-Root Variance Stabilizing Transformation for Nigeria Crude Oil Export to America. Science Journal of Applied Mathematics and Statistics, 5(5), 174-180.
Crossref
 
Akaike, H. (1974). A new look at the statistical model identification. IEEE transaction on automatic control, 19(6), 716-723.
Crossref
 
Bakari, H. R., Chamalwa, H. A., & Mohammed, A. D. (2013). Time series analysis model for production and utilization of Gas (A case study of Nigeria national petroleum corporation 'NNPC'). IOSR Journal of Mathematics (IOSR-JM), 9(1), 17-23.
Crossref
 
Box, G. E. P., & Jenkins, G. M. (1976). Time series Analysis Forecasting and Control, Revised Edition, Holden-Day. ISBN: 0816211043, San Francisco-USA.
 
Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1994). Time series Analysis Forecasting and Control, 3rd Edition, Prentice Hall, Englewood Cliffs, New Jersey-USA.
 
Bumham, K. P., & Anderson, D. R. (2002). Model selection and multimodal inference. A practical information theoretical approach, 2nd edn. Springer-Verlag, New-York, USA.
 
Central Bank of Nigeria (2015). Financial Policies by the Apex bank of Nigeria. Central Bank of Nigeria publications.
Website link
 
Dania, E. N. (2013). Determinants of inflation in Nigeria (1970–2010). The Business & Management Review, 3(2), 88-92.
 
David, A. K., & Raymond, C. E. (2016). Modelling and Forecasting CPI inflation in Nigeria: Application of Autoregressive Integrated Moving Average Homoscedastic model. Journal of Scientific and Engineering Research, 3(2), 57-66.
 
Dickey, D. A., & Fuller, W. A. (1979). Distribution of the Estimators for Autoregressive time series with a unit root. Journal of the American Statistics Association, 7(366), 427-431.
Crossref
 
Ekpenyong, E. J., & Udoudo, U. P. (2016). Short-Term Forecasting of Nigeria Inflation Rates Using Seasonal ARIMA Model. Science Journal of Applied Mathematics and Statistics, 4(3), 101-107.
Crossref
 
Engle, R. (1982). Autoregressive conditional Heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007.
Crossref
 
Etuk, H. E. (2012). Predicting Inflation Rates of Nigerian using a Seasonal Box-Jenkins Model. Journal of Statistical and Econometric Methods, 1(3), 27-37.
 
Kwiatkowski, D., Phillip, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root. Journal of Econometrics, 54, 159-178.
Crossref
 
Ljung, G. M., & Box G. E. P. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3-4), 591-611.
Crossref
 
Olajide, J. T., Ayansola, O. A., Odusina, M. T., & Oyemiga, I. F. (2012). Forecasting the inflation rate in Nigeria: Box-Jenkins approach. IOSR Journal of Mathematics (IOSR-JM), 3(5), 15-19.
Crossref
 
Osarumwense, O. I., & Waziri, E. I. (2013). Modelling Monthly Inflation Rate Volatility, using Generalised Autoregressive Conditionally Heteroscedastic (GARCH) Models. Evidence from Nigeria Australian Journal of Basic and Applied statistics, 9(12), 120-132.
 
Osuolale, P. P., Ayanniyi, W. A., Adesina, A. R., & Matthew T. O. (2017). Time series analysis to model and forecast inflation rate in Nigeria. Anale. Seria Informatica, XV(1), 174-178.
 
Otu, A. O., Osuji, G. A., Opara, J., Mbachu, H. I., & Iheagwara, A. I. (2014). Application of Sarima Models in Modelling and Forecasting Nigeria's Inflation Rates. American Journal of Applied Mathematics and Statistics, 2(1), 16-28.
Crossref
 
R core Team (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
Website link
 
Shakira, G. (2011). Time Series Analysis of Stock Prices Using the Box-Jenkins Approach. Electronic Theses & Dissertations. 668.
Website link
 
Shapiro, S. S., & Wilk, M. B. (1965). "An analysis of variance test for normality (complete samples). Biometrika, 52(3-4), 591-611.
Crossref
 
Udegbunam, E. C., & Onu, I. J. (2016). Modelling Nigeria's Urban and Rural inflation using Box-Jenkins model. Scientific papers series Management, Economic Engineering in Agriculture and Rural Development, 6(4), 61-68.
 
Yang, Y. (2005). Can the strength of AIC and BIC be shared? Biometrika, 92, 937-950.
Crossref