Probability Forecast Using Fan Chart Analysis: A case of the Sierra Leone Economy

Jackson, Emerson Abraham and Tamuke, Edmund (2018): Probability Forecast Using Fan Chart Analysis: A case of the Sierra Leone Economy.

Abstract

This article made use of ARIMAX methodology in producing probability forecast from Fan Chart analysis for the Sierra Leone economy. In view of the estimation technique used to determine best model choice for outputting the Fan Chart, the outcomes have shown the importance of Exchange Rate variable as an exogenous component in influencing Inflation dynamics in Sierra Leone. The use of Brier Score probability was also used to ascertain the accuracy of the forecast methodology. Despite inflation outcome is showing an upward trend for the forecasted periods, the probability bands (upper and lower) have also revealed the peculiarity of the Sierra Leone economy when it comes to addressing policy measures for controlling spiralling inflation dynamics.

• Item Type: MPRA Paper
• Language: English
• Keywords: Inflation Forecast; ARIMAX; Fan Chart; Brier Score; Sierra Leone
• Subjects: C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation; E - Macroeconomics and Monetary Economics > E2 - Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy > E27 - Forecasting and Simulation: Models and Applications; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E31 - Price Level ; Inflation ; Deflation; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications

References

Brier, G. (1950). Verification of forecasts expressed in terms of probabilities. Monthly Weather Review, Vol. 78: pp. 1–3.

Casillas-Olvera, G. and Bessler, D.A. (2005). Probability forecasting and central bank accountability. Journal of Policy Modeling 28 (2006): pp. 223–234. DOI:10.1016/j.jpolmod. 2005.10.004.

Clements, M. P. (2004). Evaluating the Bank of England density forecasts of inflation. Economic Journal, Vol. 114, pp. 844–866.

Gneiting, T. and Ranjan, R. (2011). Comparing Density Forecasts Using Threshold-and Quantile-Weighted Scoring Rules. Journal of Business & Economic Statistics Vol. 29(3): pp. 411-422.

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, Vol. 7(1): pp. 51-56. DOI: 10.18483/ijSci.1507. !18.

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.

Jewson, S. (2008). The problem with the Brier score. London: Risk Management Solution (RMS).

Kallon, K. (1994). An econometric analysis of inflation in Sierra Leone. Journal of African Economies, 3(2); pp. 199–230.

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/. (Accessed: 17th February, 2018).

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/~ n o s e d a l / s t a 4 5 7 / a r i m a - f o r e c a s t s -theory.pdf&ved=0ahUKEwif_trN-6zZAhVJKsAKHZRKDTk4ChAWCDkwCA&usg=AOvVaw0mQbOZUTvzHU7q98PB46fO. (Accrssed: 9th July, 2018).

Perez-Mora, N., Alomar, M.L. and Martinez-Moll, V. (2018). Spanish Energy Market: Overview Towards Price Forecast. International Journal of Energy Economics and Policy, Vol. 8(3): pp. 1-7.

Tamuke, E., Jackson, E.A. and Sillah, A. (2018). Forecasting Inflation in Sierra Leone Using ARIMA and ARIMAX: A Comparative Evaluation. Journal of Theoretical and Practical Research in Economic Fields, Vol. 9(1): pp. 63-74.

Taylor, J. W. (2008). A Comparison of Univariate Time Series Methods for Forecasting Intraday Arrivals at a Call Center. Management Science, Vol. 54: pp. 253 - 265.

Wallis, K. F. (2004). An assessment of Bank of England and National Institute inflation forecast uncertainties. Mimeo, University of Warwick.