Forecasting Infation in Ghana using Particle Swarm Optimization (PSO)
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Date
June, 2016
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Abstract
The phenomenon of in
ation has proven to be both important and unpredictable.
The goal of every Central Bank is to achieve and maintain a desirable rate of
in
ation in a scal year. Major macroeconomic policies pertaining to minimizing
in
ation are implemented based on predictions made into the future. In this
study the Particle Swarm Optimization (PSO) intelligent method was used to
make in-sample forecasts of in
ation gures over a period. The Generalized
Autoregressive Conditional Heteroscedastic (GARCH) model was used to obtain
an in
ation model. The proposed method was implemented using Matlab (2012a)
and forecasts error were measured using MSE, MAPE and MAD. The results
obtained revealed that the method yielded lower errors for the in
ationary data
sets used.
Description
A thesis submitted to the Department of Mathematics,
Kwame Nkrumah University of Science and Technology in
partial fulfillment of the requirement for the degree
of M.Phil. Applied Mathematics