Forecasting the COVID-19 Pandemic’s Effect on Unemployment in Malaysia

Nur Syazatul A. Abdul Aziz and Chuan Hui Foo
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Nur Syazatul A. Abdul Aziz: Department of Mathematics, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris, 35900 UPSI Tanjung Malim, Perak, Malaysia
Chuan Hui Foo: Department of Mathematics, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris, 35900 UPSI Tanjung Malim, Perak, Malaysia

International Journal of Research and Innovation in Social Science, 2024, vol. 8, issue 12, 464-476

Abstract: This study aims to forecast Malaysia’s unemployment rates using the Autoregressive Integrated Moving Average (ARIMA) model, a popular tool for analyzing time-series data. Monthly unemployment data from January 2010 to October 2022, obtained from the Malaysia Labour Market Interactive Data portal, was analyzed using the Box-Jenkins methodology. After testing several models, ARIMA(2, 1, 0) was identified as the best-fit model based on diagnostic tests, including the Ljung-Box test, and model evaluation criteria such as Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). The forecast from November 2022 to October 2023, following the pandemic period, indicates a slight increase in Malaysia’s unemployment rate. These findings are significant as they provide a valuable tool for policymakers and private sectors to anticipate economic challenges and develop strategies to address potential rises in unemployment. The forecasted data can assist the government in planning job creation and workforce development initiatives to mitigate unemployment’s economic and social impacts. This study also underscores the ARIMA model’s usefulness in short-term forecasting, offering insights into future trends for better financial planning.

Date: 2024
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