• Edmund TAMUKE Research Department, Bank of Sierra Leone
  • Emerson Abraham JACKSON Research Department, Bank of Sierra Leone
  • Abdulai SILLAH Research Department, Bank of Sierra Leone


The study has provided empirical investigation of both ARIMA and ARIMAX methodology as a way of providing forecast of Headline Consumer Price Index (HCPI) for Sierra Leone based on data collected from the Sierra Leone Statistical Office and the Bank of Sierra Leone. In this, the main research question of addressing outcomes from in and out-of-sample forecast were provided using the Static technique and this shows that both methodologies were proved to have tracked past and future occurrences of HCPI with minimal margin of error as indicated in the MAPE results. In a similar note, the key objective of identifying whether the ARIMAX methodology or the ARIMA methodology is a better predictor of forecasting future trends in HCPI. However, on the whole, both ARIMA and ARIMAX seem to have provided very good outcome in predicting future events of HCPI, particularly when Static technique is used as the option for forecasting outcomes, with the ARIMAX marginally coming out as the preferred choice on the basis of its evaluation outcomes.


[1] Adebiyi, A.A., Adewumi, A.O., and Ayo, C.K. 2014. Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction. Journal of Applied Mathematics, 2014: 1-7. DOI: 10.1155/2014/614342
[2] Andrews, B.H., Dean, M.D., Swain, R., and Cole, C. 2013. Building ARIMA and ARIMAX Models for Predicting Long-Term Disability Benefit Application Rates in the Public/Private Sectors. Society of Actuaries, University of Southern Maine.
[3] Bigovic, M. 2012. Demand Forecasting within Montenegrin Tourism using Box-Jenkins Methodology for Seasonal ARIMA models. Tourism and Hospitality Management, 18(1): 1–18.
[4] Box, G. E. P., and Jenkins, G. M. 1976. Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day Inc.
[5] Coshall, J. T. 2005. A Selection Strategy for Modelling UK Tourism Flows by Air to European Destinations. Tourism Economics, 11: 141–158.
[6] Dash, S.R., and Dash, S. 2017. Application of ARMA Models for Measuring Capital Market Efficiency: An Empirical Study in Selected Emerging Financial Markets. International Journal of Applied Business and Economic Research, 15(21): 175-188.
[7] Green, S. 2011. Time Series Analysis of Stock Prices Using the Box-Jenkins Approach, Master’s Thesis submitted to College of Graduate Studies, Georgia Southern University.
[8] Grunfeld, Y., and Griliches, Z. 1960. Is Aggregation Necessarily Bad? The Review of Economics and Statistics. 42: 1 – 13.
[9] Hamjah, M.A. 2014. Climatic Effects on Major Pulse Crops Production in Bangladesh: An Application of Box-Jenkins ARIMAX Model. Journal of Economics and Sustainable Development, 5(15): 169 – 180.
[10] Huang, J. H., and Min, J. C. H. 2002. Earthquake Devastation and Recovery in Tourism: the Taiwan Case. Tourism Management, 23: 145–154.
[11] Hubrich, K. 2005. Forecasting Euro Area Inflation: Does Aggregation Forecast by HICP Component Improve Forecast Accuracy? International Journal of Forecasting, 21: 119 – 136.
[12] Jackson, E.A. 2018. Comparison between Static and Dynamic Forecast in Autoregressive Integrated Moving Average for Seasonally Adjusted Headline Consumer Price Index. University of Munich RePEc Archive. MPRA_Paper_86180.
[13] Jackson, E.A., Sillah, A. and Tamuke, E. 2018. Modelling Monthly Headline Consumer Price Index (HCPI) through Seasonal Box-Jenkins Methodology. International Journal of Sciences, 7(1): 51-56. DOI: 10.18483/ijSci.1507
[14] Kongcharoen, C. and Kruangpradit, T. 2013. Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) Model for Thailand Export. 33rd International Symposium on Forecasting, South Korea.
[15] Kravchuk, K. 2017. Forecasting: ARIMAX Model Exercises (Part-5). Available at:
[16] Kulendran, N., and Witt, S. F. 2001. Cointegration Versus Least Squares Regression. Annals of Tourism Research, 28: 291–311.
[17] Law, R. 2004. Initially Testing an Improved Extrapolative Hotel Room Occupancy Rate Forecasting Technique. Journal of Travel & Tourism Marketing, 16: 71–77.
[18] Nosedal, A. 2016. Univariate ARIMA Forecasts (Theory). University of Toronto. Available at:
[19] Paul, J.C., Hoque, M.S., and Rahman, M. 2013. Selection of Best ARIMA Model for Forecasting Average Daily Share Price Index of Pharmaceutical Companies in Bangladesh: A Case Study on Square Pharmaceutical Ltd., Global Journal of Management and Business Research Finance, 13(3): 15 – 25.
[20] Peter, D., and Silvia, P. 2012. ARIMA Vs. ARIMAX – Which Approach is Better to Analyze and Forecast Macroeconomic Variables?, Proceedings of 30th International Conference Mathematical Methods in Economics.
[21] Slutsky, E. E. 1927. Slozhenie sluchainykh prichin, kak istochnik tsiklicheskikh protsessov. Voprosy kon”yunktury, 3: 34–64.
[22] Stock, J.H. and Watson, M.W. 2003. Introduction to Econometrics. Addison Wesley.
[23] Theil, H. 1954. Linear Aggregation of Economic Relations. Amsterdam: North Holland.
[24] Williams, B. 2001. Multivariate Vehicular Traffic Flow Prediction: Evaluation of ARIMAX Modeling. Journal of the Transportation Research Board, 1776: 194-200. DOI: 10.3141/1776-25
[25] Wold, H. 1938. A Study in the Analysis of Stationary Time Series. Doctoral Thesis, Uppsala: Almqvist & Wiksell.
How to Cite
TAMUKE, Edmund; JACKSON, Emerson Abraham; SILLAH, Abdulai. FORECASTING INFLATION IN SIERRA LEONE USING ARIMA AND ARIMAX: A COMPARATIVE EVALUATION. MODEL BUILDING AND ANALYSIS TEAM. Theoretical and Practical Research in the Economic Fields, [S.l.], v. 9, n. 1, p. 63-74, sep. 2018. ISSN 2068-7710. Available at: <>. Date accessed: 20 jan. 2019.