Forecasting Unemployment Rate in Malaysia Using Box-Jenkins Methodology during Covid-19 Outbreak

Abstract

The economic dimension has revealed that weak economic growth is one of the causes of unemployment and is related to GDP. Forecasting the unemployment rate is essential and is an important determinant of monetary policy decisions and needs to be addressed. This study is conducted to identify whether novel coronavirus 2019, COVID-19 affects Malaysia’s unemployment rate and forecast the rate for the next two years. The Box-Jenkins approach uses the Augmented Dickey-Fuller test to stabilize the data. After reducing the trend pattern of the partial autocorrelation coefficient, the ARIMA prediction method ARIMA (2,1,2) was selected as the best model to apply to the unemployment rate time series data. As a result, the projected unemployment rate graph showed a steady increase over the next two years. For future research, it is recommended to consider factors such as inflation, growth domestic product and employment to predict this value to improve results.