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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/10598

Title: Inflation Forecasting in Ghana-Artificial Neural Network Model Approach
Authors: Yusif, M. Hadrat
Eshun Nunoo, Isaac K.
Effah Sarkodie, Eric
Keywords: Inflation
Artificial neural network
Demand
Supply
Issue Date: 2015
Publisher: International Journal of Economics & Management Sciences
Citation: International Journal of Economics & Management Sciences, Vol.4,No. 8, 2015
Abstract: Artificial Neural Network (ANN) is a modelling technique which is based on the way the human brain process information. ANNs have proved to be good forecasting models in several fields including economics and finance. The ANN methodology is used by some central banks to predict various macroeconomic indicators such as the inflation, money supply, GDP growth etc. The use of the ANN for prediction is common in the forecasting literature but rare in Ghana. This paper forecasts inflation with the ANN method using the Ghanaian data. The monthly y-o-y data between 1991:01 and 2010:12 are used to estimate and forecast for the period 2011:01 to 2011:12. The result of the ANNs are also compared with traditional time series models such as the AR (12) and VAR (14) which use the same set of variables. The basis of comparison is the out-of-sample forecast error (RMSFE). The results show that the RMSFE of the ANNs are lower than their econometric counterparts. That is, by this comparative criterion forecast based on ANN models are more accurate.
Description: An article published by International Journal of Economics & Management Sciences, Vol.4,No. 8, 2015
URI: http://hdl.handle.net/123456789/10598
Appears in Collections:College of Arts and Social Sciences

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