FORECASTING INFLATION IN SIERRA LEONE USING ARIMA AND ARIMAX: A COMPARATIVE EVALUATION. MODEL BUILDING AND ANALYSIS TEAM
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.
 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.
 Bigovic, M. 2012. Demand Forecasting within Montenegrin Tourism using Box-Jenkins Methodology for Seasonal ARIMA models. Tourism and Hospitality Management, 18(1): 1–18.
 Box, G. E. P., and Jenkins, G. M. 1976. Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day Inc.
 Coshall, J. T. 2005. A Selection Strategy for Modelling UK Tourism Flows by Air to European Destinations. Tourism Economics, 11: 141–158.
 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.
 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.
 Grunfeld, Y., and Griliches, Z. 1960. Is Aggregation Necessarily Bad? The Review of Economics and Statistics. 42: 1 – 13.
 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.
 Huang, J. H., and Min, J. C. H. 2002. Earthquake Devastation and Recovery in Tourism: the Taiwan Case. Tourism Management, 23: 145–154.
 Hubrich, K. 2005. Forecasting Euro Area Inflation: Does Aggregation Forecast by HICP Component Improve Forecast Accuracy? International Journal of Forecasting, 21: 119 – 136.
 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.
 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
 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.
 Kravchuk, K. 2017. Forecasting: ARIMAX Model Exercises (Part-5). Available at: https://www.google.co.uk/amp/s/www.r-bloggers.com/forecasting-arimax-model-exercises-part-5/amp/
 Kulendran, N., and Witt, S. F. 2001. Cointegration Versus Least Squares Regression. Annals of Tourism Research, 28: 291–311.
 Law, R. 2004. Initially Testing an Improved Extrapolative Hotel Room Occupancy Rate Forecasting Technique. Journal of Travel & Tourism Marketing, 16: 71–77.
 Nosedal, A. 2016. Univariate ARIMA Forecasts (Theory). University of Toronto. Available at: https://www.google.co.uk/url?sa=t&source=web&rct=j&url=https://mcs.utm.utoronto.ca/~nosedal/sta457/arima-forecasts-theory.pdf&ved=0ahUKEwif_trN-6zZAhVJKsAKHZRKDTk4ChAWCDkwCA&usg=AOvVaw0mQbOZUTvzHU7q98PB46fO
 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.
 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.
 Slutsky, E. E. 1927. Slozhenie sluchainykh prichin, kak istochnik tsiklicheskikh protsessov. Voprosy kon”yunktury, 3: 34–64.
 Stock, J.H. and Watson, M.W. 2003. Introduction to Econometrics. Addison Wesley.
 Theil, H. 1954. Linear Aggregation of Economic Relations. Amsterdam: North Holland.
 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
 Wold, H. 1938. A Study in the Analysis of Stationary Time Series. Doctoral Thesis, Uppsala: Almqvist & Wiksell.
The Copyright Transfer Form to ASERS Publishing (The Publisher)
This form refers to the manuscript, which an author(s) was accepted for publication and was signed by all the authors.
The undersigned Author(s) of the above-mentioned Paper here transfer any and all copyright-rights in and to The Paper to The Publisher. The Author(s) warrants that The Paper is based on their original work and that the undersigned has the power and authority to make and execute this assignment. It is the author's responsibility to obtain written permission to quote material that has been previously published in any form. The Publisher recognizes the retained rights noted below and grants to the above authors and employers for whom the work performed royalty-free permission to reuse their materials below. Authors may reuse all or portions of the above Paper in other works, excepting the publication of the paper in the same form. Authors may reproduce or authorize others to reproduce the above Paper for the Author's personal use or for internal company use, provided that the source and The Publisher copyright notice are mentioned, that the copies are not used in any way that implies The Publisher endorsement of a product or service of an employer, and that the copies are not offered for sale as such. Authors are permitted to grant third party requests for reprinting, republishing or other types of reuse. The Authors may make limited distribution of all or portions of the above Paper prior to publication if they inform The Publisher of the nature and extent of such limited distribution prior there to. Authors retain all proprietary rights in any process, procedure, or article of manufacture described in The Paper. This agreement becomes null and void if and only if the above paper is not accepted and published by The Publisher, or is with drawn by the author(s) before acceptance by the Publisher.