Time series arima modelling of inflation in Ghana: (1990 – 2009)
| dc.contributor.author | Owusu, Frank Kofi | |
| dc.date.accessioned | 2011-08-14T21:23:20Z | |
| dc.date.accessioned | 2023-04-20T13:28:43Z | |
| dc.date.available | 2011-08-14T21:23:20Z | |
| dc.date.available | 2023-04-20T13:28:43Z | |
| dc.date.issued | 2010-08-14 | |
| dc.description | A Thesis submitted to the Department of Mathematics,Kwame Nkrumah University of Science and Technology in partial fulfillment of the requirements for the degree of Master of Science. | en_US |
| dc.description.abstract | Throughout the world, most central bank policy initiatives have been aimed at achieving and maintaining price stability and the Bank of Ghana is no exception to this rule. This study attempts to outline the practical steps which need to be undertaken in order to use the autoregressive integrated moving average (ARIMA) model for forecasting Ghana’s inflation. The main focus of the study is to model inflation and hence used to forecast the monthly inflation on short-term basis, for this purpose, different ARIMA models are used and the candid model is selected based on various diagnostic, evaluation and selection criteria. It can be concluded that the model has sufficient predictive powers and the findings are well in line with those of other studies. Again the study models inflation for the periods of 1990 to 2000 and 2001 to 2009 and it was realized that the inflation model for the period of 1990 to 2000 is ARIMA (1, 2,2) written as (y_t ) ̂=18.5770+0.455848t-3.57e^(-0.3) t^2+0.7807y_(t-1)-1.0813ε_(t-1)+0.1020ε_(t-2)+(ε_t ) ̂. Whilst that of 2001 to 2009 is modelled as ARIMA (2, 2, 1), written as (y_t ) ̂=34.3958-0.637228t+4.40e^(-0.3) t^2-1.3764y_(t-1)-0.4389y_(t-2)+0.9860ε_(t-1)+(ε_t ) ̂ It was concluded that inflation for the period of January 2001 to December 2009 was less than that of January 1990 to December 2000. The model is recommended for use by stakeholders because it has a lower error variance of ±1 which follows closely with the actual data. It is recommended further to be used as the basis for constructing deterministic models such as first and second order differential equations by future researchers | en_US |
| dc.description.sponsorship | KNUST | en_US |
| dc.identifier.uri | https://ir.knust.edu.gh/handle/123456789/866 | |
| dc.language.iso | en | en_US |
| dc.title | Time series arima modelling of inflation in Ghana: (1990 – 2009) | en_US |
| dc.type | Thesis | en_US |
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