Forecast Performance Between SARIMA and SETAR Models: An Application to Ghana Inflation Rate
In recent years, many research works such as Tiao and Tsay (1994), Stock and Watson (1999), Chen et al. (2001), Clements and Jeremy (2001), Marcellino (2002), Laurini and Vieira (2005) and others have described the dynamic features of many macroeconomic variables as nonlinear. Using the approach of Keenan (1985) and Tsay (1989) this study shown that Ghana inflation rates from January 1980 to December 2009 follow a threshold nonlinear process. In order to take into account the nonlinearity in the inflation rates we then apply a two regime nonlinear SETAR model to the inflation rates and then study both in-sample and out-of-sample forecast performance of this model by comparing it with the linear SARIMA model.
Based on the in-sample forecast assessment from the linear SARIMA and the nonlinear SETAR models, the forecast measure MAE and RMSE suggest that the nonlinear SETAR model outperform the linear SARIMA model. Also using multi-step-ahead forecast method we predicted and compared the out-of-sample forecast of the linear SARIMA and the nonlinear SETAR models over the forecast horizon of 12 months during the period of 2010:1 to 2010:12. From the results as suggested by MAE and RMSE, the forecast performance of the nonlinear SETAR models is superior to that of the linear SARIMA model in forecasting Ghana inflation rates.
Thought the nonlinear SETAR model is superior to the SARIMA model according to MAE and RMSE measure but using Diebold-Mariano test, we found no significant difference in their forecast accuracy for both in-sample and out-of-sample.
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