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ISSN: 2162-6359

International Journal of Economics & Management Sciences
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Research Article

Inflation Forecasting in Ghana-Artificial Neural Network Model Approach

Yusif M Hadrat1, Eshun Nunoo Isaac K2 and Effah Sarkodie Eric3*

1Department of Economics, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

2Ghana College, Kumasi, Ghana

3University of Education, Winneba, College of Technology Education, Department of Accounting Studies Education, Kumasi, Ghana

*Corresponding Author:
Effah Sarkodie Eric
M. Phil. Economics, B. A. Economics
University of Education, Winneba
College of Technology Education
Department of Accounting Studies Education,Kumasi, Ghana
Tel: 03220-53602
E-mail: [email protected]

Received Date: July 14, 2015; Accepted Date: July 30, 2015; Published Date: August 05, 2015

Citation: Hadrat YM, Eshun Nunoo Isaac K, Eric ES (2015) Inflation Forecasting in Ghana-Artificial Neural Network Model Approach. Int J Econ Manag Sci 4:274. doi:10.4172/2162-6359.1000274

Copyright: © 2015 Hadrat YM, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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.

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