Determining the Better Approach for Short-Term Forecasting of Ghana’s Inflation: Seasonal-ARIMA vs. Holt-Winters
Maurice Omane-Adjepong, Francis T. Oduro, Samuel Dua Oduro

Abstract
In this paper, we examine the most appropriate short-term forecasting method for Ghana’s inflation. A monthly inflation data which spans from January 1971 to October 2012 was obtained from Ghana Statistical Service. The data was divided into two sets: the first set was used for modelling and forecasting, whiles the second was used as test set. Seasonal-ARIMA and Holt-Winters approaches were used to achieve short-term out-of-sample forecast. The accuracy of the out-of-sample forecast was measured using MAE, RMSE, MAPE and MASE. Empirical results from the study indicate that the Seasonal-ARIMA forecast from ARIMA(2,1,1)(0,0,1)12 recorded MAE, RMSE, MAPE and MASE of 0.1787, 0.2104, 1.9123 and 0.0073 respectively; that of the Seasonal Additive HW was 1.8329, 2.0176, 19.996, 0.0745; and the Seasonal Multiplicative HW forecast recorded 2.2305, 2.4274, 24.000, 0.0911 respectively. Based on these results, we conclude by proposing the Seasonal-ARIMA process as the most appropriate short-term forecasting method for Ghana’s inflation

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