A Comparative Analysis of Forecast Performance between Sarima and Setar Models Using Macroeconomic Variables in Ghana
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Date
2018-07
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University of Ghana
Abstract
Most 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.
Description
Thesis (MPhil) -University of Ghana
Keywords
Forecast, Performance, Macroeconomic, Models, Inflation, Financial