Forecasting the unemployment rate in Malaysia during COVID-19 pandemic using arima and arfima models / Nur Afiqah Ismail, Nurin Alya Ramzi and Pauline Jin Wee Mah - Ismail, Nur Afiqah; Ramzi, Nurin Alya; Wee Mah, Pauline Jin (Universiti Teknologi MARA, 2022)
The unemployment issue is one of the most common problems faced by many countries around the world. The unemployment rates in developed countries often fluctuate throughout time. Similarly, Malaysia is also affected by the inconsistent unemployment rate especially during the COVID-19 pandemic. Therefore, in order to understand the trend better, ARIMA and ARFIMA were used to model and forecast the unemployment rate in Malaysia in this study. The dataset on the unemployment rate in Malaysia from January 2010 until July 2021 was obtained from Bank Negara Malaysia (BNM) official portal. The best time series models found were ARIMA (2, 1, 2) and ARFIMA (0, −0.2339, 0). The performance of the models was evaluated using mean absolute percentage error (MAPE), mean absolute error (MAE) and root mean square error (RMSE). It appeared that the ARFIMA model emerged as a better forecast model since it had better performance compared to ARIMA in forecasting the unemployment rate in Malaysia.