Forecasting Infation in Ghana using Particle Swarm Optimization (PSO)
| dc.contributor.author | Atiso, Francis | |
| dc.date.accessioned | 2016-10-03T13:07:28Z | |
| dc.date.accessioned | 2023-04-21T10:37:02Z | |
| dc.date.available | 2016-10-03T13:07:28Z | |
| dc.date.available | 2023-04-21T10:37:02Z | |
| dc.date.issued | June, 2016 | |
| dc.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 | en_US |
| dc.description.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. | en_US |
| dc.description.sponsorship | KNUST | en_US |
| dc.identifier.uri | https://ir.knust.edu.gh/handle/123456789/9053 | |
| dc.language.iso | en | en_US |
| dc.title | Forecasting Infation in Ghana using Particle Swarm Optimization (PSO) | en_US |
| dc.type | Thesis | en_US |
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