A Comparative Analysis of Forecast Performance between Sarima and Setar Models Using Macroeconomic Variables in Ghana

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dc.contributor.authorAhmed, N.B.
dc.date.accessioned2019-02-05T15:42:35Z
dc.date.available2019-02-05T15:42:35Z
dc.date.issued2018-07
dc.descriptionThesis (MPhil) -University of Ghanaen_US
dc.description.abstractMost macroeconomic variables such as; inflation, GDP and others have been described by most financial and economics time series analysts to exhibit nonlinear behaviour. Therefore, to cater for this behaviour, the nonlinear class of models have been largely adopted to model and forecast such time series. In this study, the Keenan and Tsay tests for linearity showed inflation and CIC rates follow threshold nonlinear processes. Hence, the two-regime SETAR model was adopted to accommodate these nonlinearities in the datasets. Using the linear SARIMA model as a benchmark for comparative analysis. Results from both in-sample and out- of- sample forecast performance using MAE and RMSE measures revealed that, the nonlinear SETAR model outperformed the linear SARIMA model for inflation. This was however different for CIC rates, since the Linear SARIMA model turned to outperform the nonlinear SETAR model. Further analysis of forecast accuracy using the Diebold- Mariano test showed there was no significant difference between the two models for inflation but, there was significant difference between both models for CIC rates. Nevertheless, it is recommended that, continuous monitoring of these models, review market conditions and necessary adjustments are vital to make realistic use of these models.en_US
dc.identifier.urihttp://ugspace.ug.edu.gh/handle/123456789/27259
dc.language.isoenen_US
dc.publisherUniversity of Ghanaen_US
dc.subjectForecasten_US
dc.subjectPerformanceen_US
dc.subjectMacroeconomicen_US
dc.subjectModelsen_US
dc.subjectInflationen_US
dc.subjectFinancialen_US
dc.titleA Comparative Analysis of Forecast Performance between Sarima and Setar Models Using Macroeconomic Variables in Ghanaen_US
dc.typeThesisen_US

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