Abstract

The study focuses on developing a model of short-term inflation forecasting (STIF) in the context of monetary policy conceptualization. The research toolkit is accompanied using the Box-Jenkins Time Series Approach (ARIMA) to analyze inflation and its risks in Sierra Leone. The proposed model is based on diagrams characterizing the constituents of the Consumer Price Index (CPI) as the primary means of communication to inform the general public about the uncertainty surrounding the dynamics of prices in Sierra Leone. The author points out that such an approach to monetary policy forecasting will allow the use of expert judgment to stabilize the economy in the formation of key areas of monetary policy functioning and development. The article focuses on the interpretation of risks for each of the components of the CPI, which will enhance confidence in the financial and economic decisions made by the financial management of the Bank of Sierra Leone [BSL]. Empirical findings have shown that CPI components such as food and non-alcoholic beverages, housing and health indicate that financial shocks occurring in or outside Sierra Leone can significantly affect the overall CPI. It is stated that such a trend causes a fleeting effect of rising prices on consumer spending in the short term. The study postulates the uniqueness of the component model in the context of turning the key focus of financial management of business structures and government organizations to target sectoral events. As a result of the study, the conclusion is drawn about the need to monitor price volatility by empirically evaluating the components of the CPI basket in the forward-looking perspective. The importance of forecast is been resounded all around the globe, given its relevance in addressing the dynamics of macroeconomic variables and their significant implications for economic stabilisation and decision-making.

Keywords: financial management, inflation, forecasting, consumer price index, CPI components; Sierra Leone.

JEL Classification: C13, C52, C53, E37.

Cite as: Jackson, E.A., Tamuke E., Jabbie M. (2019). Disaggregated Short-Term Inflation Forecast (STIF) for Monetary Policy Decision in Sierra Leone. Financial Markets, Institutions and Risks, 3(4), 32-48. http://doi.org/10.21272/fmir.3(4).32-48.2019.

References

1. Bangura, M., Caulker, E. and Pessima, S. (2012). Exchange Rate Pass-Through to Inflation in Sierra Leone: A Structural Vector Autoregressive Approach, Journal of Monetary and Economic Integration, 12(1), 93-123.
2. Bank of Sierra Leone. (2019). The Bank of Sierra Leone Act 2019. Available at: http://www.bsl.gov.sl/Bank%20of%20Sierra%20Leone%20Ascent%202019.pdf. (Accessed: 30th July, 2019).
3. Benalal, N., Diaz del Hoyo, J.L., Landau, B., Roma, M. and Skudelny, F. (2004). To aggregatet or not to aggregate? Euro area inflation forecasting. Working Paper 374, European Central Bank.
4. Box, G. E. P., and Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day Inc.
5. Coleman, W. O. (2007). The Cause, Costs and Compensation of Inflation, Edward Elgar.
6. Kji Espasa, A., Senra, E. and Albacete, R. (2002). Forecasting Inflation in the European Monetary Union: A disaggregated approach by countries and by sectors. European Journal of Finance, 8(4), 402-421.
7. Gordon, J.R.J. (1977). The Theory of Domestic Inflation, Amsterdam, Elsevier Science, Journal of Economic Review, 67(1), 128–34.
8. Harvey, S.K. (2012). Essays in inflation and monetary dynamics in developing countries. Doctoral Thesis, University of Nebraska, Lincoln.
9. Humphrey, T.M. (1998). Historical Origins of the Cost-Push Fallacy, Richmond, Federal Reserve Bank of Richmond Economic Quarterly, 84(3), 53-74.
10. Jackson, E.A., Jabbie, M., and Tamuke, E. (forthcoming). Dynamic effect of inflation shocks in Sierra Leone: An empirical analysis.
11. Jackson, E.A., and Tamuke, E. (forthcoming). Predicting disaggregated tourist arrivals in Sierra Leone using ARIMA Model.
12. Jackson, E.A. (2019). Systemic Health Care Failure as a Symptom of Market Failure in Sierra Leone. South African Journal of Public Health, 3(4), 72-78.
13. Jackson, E.A., and Jabbie, M. (forthcoming). Twin Deficits hypothesis as an indication of government failure in Sierra Leone: An empirical investigation (2007 to 2018).
14. Jackson, E.A., and Tamuke, E. (2019). Predicting Disaggregated Tourist Arrivals in Sierra Leone using ARIMA Model. DOI: 10.20944/preprints201909. 0102.v1). Jackson.
15. E.A. and Jabbie, M. (2019). Understanding Market Failure in the Developing Country Context (Online First). In, Walter L. Filho (eds), Decent Work and Economic Growth: Encyclopedia of Sustainable Development Goals, Springer Nature Publisher. DOI: 10.1007/978-3-319-71058-7_44-1.
16. Jackson, E.A. (2018). Comparison between Static and Dynamic Forecast in Autoregressive Integrated Moving Average for Seasonally Adjusted Headline Consumer Price Index. Available at SSRN: http://dx.doi.org/10.2139/ssrn.3162606.
17. Jackson, E.A., Tamuke, E., and Sillah, A. (2018). Modelling Monthly Headline Consumer Price Index (HCPI) through Seasonal Box-Jenkins Methodology. International Journal of Sciences, 7 (1), 51-56.
18. Jackson, E.A. and Tamuke, E. (2018). Probability Forecast Using Fan Chart Analysis: A Case of the Sierra Leone Economy. Journal of Advanced Studies in Finance, 9(1), 34-44.
19. Hubrich, K. (2005). Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy? International Journal of Forecasting, 21(1), 119-136.
20. Lim, C.H and Papi, L. (1997). An Econometric Analysis of the Determinants of Inflation in Turkey. IMF Working Paper, No. WP/97/170.
21. Mansaray-Pearce, E.A. and Liu, P. (2015). The Determinants of Inflation in Sierra Leone: A Cointegration Analysis. Journal of Economics and Sustainable Development, 6(6), 121-131.
22. Kallon, K. (1994). An econometric analysis of inflation in Sierra Leone. Journal of African Economies, 3(2): 199–230.
23. King, M. (2005). Monetary Policy: Practice Ahead of Theory. Bank of England. Mais Lecture of Economics and Finance, 3-4/2008.
24. Mansaray-Pearce, E.A. and Liu, P. (2015). The Determinants of Inflation in Sierra Leone: A Cointegration Analysis. Journal of Economics and Sustainable Development, 6(6), 121-131.
25. Montiel, P.J. (1989). Empirical Analysis of High-Inflation Episodes in Argentina and Brazil, Washington. D. C, International Monetary Fund (IMF), Staff Papers, 36(3), 527–549.
26. Mordi, C.N.O., Adebiyi, M.A., and Adamgbe, E.T. (2012). Short Term Inflation Forecasting for Monetary Policy In Nigeria. Central Bank of Nigeria (CBN).
27. Mwenese, B., and Kwizera, A.P. (2018). Modelling and Forecasting Inflation Dynamics in Rwanda. BNR Economic Review, 13, 45 – 71.
28. Prescatori, A. and S. Zaman. (2011). Macroeconomic Models, Forecasting and Policymaking. Economic Commentary No. 19. Cleveland: Federal Reserve Bank of Cleveland.
29. Rodrik, D. (1991). Premature Liberalization, Incomplete Stabilization: The Ozal Decade in Turkey, in Lessons of Economic Stabilization and its Aftermath, ed. M. Bruno, S. Fischer, E. Helpman, and N. Liviatan with L. Meridor (Cambridge, Mass, and London: MIT Press).
30. Rummel, O. (2011). A Short-term inflation forecast for South Africa, Economic Modelling and Forecasting, Pages 1-15. Centre for Central Banking Studies, Bank of England.
31. Salam, M. A., S. Salam and Feridun, M. (2006). Forecasting Inflation in Developing Nations: The Case of Pakistan, International Research Journal of Finance and Economics, EuroJournals Publishing, Inc., 3.
32. Sargent, T.J and Wallace, N. (1981). Some Unpleasant Monetarist Arithmetic”, Minneapolis, Federal Reserve Bank of Minneapolis, Journal of Quarterly Review, 5, 1-17.
33. Sims, C.A. (1980). A Comparison of Interwar and Postwar Business Cycles: Monetarism Reconsidered, Amsterdam, Elsevier Science, and Journal of Economic Review, Annual Papers and Proceedings 70, 250-257.
34. Statistics Sierra Leone [SSL] (2018). Consumer Price Index December 2018 Press Release. Available at: https://www.statistics.sl/images/StatisticsSL/Documents/cpi/2018/cpi_press_release_december_2018.pdf. (Accessed: 8th February, 2019).
35. Tamuke, E., Jackson, E.A. and Sillah, A. (2018). Forecasting inflation in Sierra Leone using ARIMIA and ARIMAX: A comparative evaluation. Theoretical and Practical Research in Economic Field, 1(17), 63-74.
36. Totonchi, J. (2011). Macroeconomic Theories of Inflation. International Conference on Economics and Finance Research (IPEDR), 4, 459 – 462.