FORECASTING THE UNEMPLOYMENT RATE IN MALAYSIA DURING COVID-19 PANDEMIC USING ARIMA AND ARFIMA MODELS

Authors

  • Nur Afiqah Ismail Faculty of Computer and Mathematical Science, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
  • Nurin Alya Ramzi Faculty of Computer and Mathematical Science, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
  • Pauline Jin Wee Mah Faculty of Computer and Mathematical Science, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia

DOI:

https://doi.org/10.24191/mjoc.v7i1.14641

Keywords:

ARFIMA, ARIMA, Unemployment Rate, Univariate Time Series

Abstract

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.

References

Azman, N. H. (2021). Unemployment May Improve to 4%. The Malaysian Reserve. Retrieved on 9 April, 2021, from https://themalaysianreserve.com/2021/04/09/unemployment-mayimprove-to-4/.

Bank Negara Malaysia (BNM) Official Portal. (2021). Unemployment Monthly (% of Labour Force). [Data set]. Department of Statistics Malaysia. Retrieved from https://www.bnm.gov.my/documents/20124///9f0226bb-85ea-6748-7742-b71e650d899f/

Bhansali, R. J., & Kokoszka, P. S. (2003). Prediction of Long-Memory Time Series: A Tutorial Review. Processes with Long-Range Correlations, 3-21. https://doi.org/10.1007/3-540-44832-2_1.

Box, G.E.P., & Cox, D.R. (1964). An Analysis of Transformations (with discussion), J. R. Stat. Soc. B, 26, 211–252.

Brockwell, P. J., & Davis, R. A. (2016). Introduction to Time Series and Forecasting, Third Edition. New York: Springer.

Didiharyono, D., & Bakhtiar, B. (2018). Forecasting Model with Box-Jenkins Method to Predict Tourists Who Visit Tourism Place in Toraja. Journal of Economic, Management and Accounting, 1(1), 34–41. Retrieved on 11 May, 2021, from http://ojs.unanda.ac.id/index.php/jemma/article/download/75/65.

Didiharyono, D., & Syukri, M. (2020). Forecasting with ARIMA Model in Anticipating Open Unemployment Rates in South Sulawesi. International Journal of Scientific and Technology Research, 9(3), 3838-3841.

Department of Statistics Malaysia (DOSM) Official Portal (2020). Retrieved on 4 May, 2021, from Gov.my website: https://www.dosm.gov.my.

Farrelly, C. (2017). What Is the Importance and Usage of Time Series. Retrieved on 27 April, 2019, from https://www.quora.com/What-is-the-importance-and-usage-of-Time-Series.

Hossain, M. I., Yagamaran, K. S. A., Afrin, T., Limon, N., Nasiruzzaman, M., & Karim, A. M. (2018). Factors Influencing Unemployment Among Fresh Graduates: A Case Study in Klang Valley, Malaysia. International Journal of Academic Research in Business and Social Sciences, 8(9), 1494-1507. http://dx.doi.org/10.6007/IJARBSS/v8-i9/4859.

Kurita, T. (2010). A Forecasting Model for Japan's Unemployment Rate. Eurasian Journal of Business and Economics, 3(5), 127-134.

Lazim, M. A. (2018). Introductory Business Forecasting: A Practical Approach. UiTM Press, Kuala Lumpur.

Lip, N. B. M., Rasyid, N. R. M., Rizuan, N. L. N. M., Iezudin, N. I., Mohamad, N. A., & Ithnin, H. B. (2021). Comparative Study of Smoothing Methods and Box-Jenkins Model in Forecasting Unemployment Rate in Malaysia. Gading Journal of Science and Technology, 4(1), 1-8.

Mah, P. J. W., Zali, N. N. M., Ihwal, N. A. M., & Azizan, N. Z. (2018). Forecasting Fresh Water and Marine Fish Production in Malaysia using ARIMA and ARFIMA Models. Malaysian Journal of Computing, 3(2), 81-92.

Mahmudah, U. (2017). Predicting Unemployment Rates in Indonesia. Economic Journal of Emerging Markets, 9(1), 20-28.

McLeod, A. I., & Li, W. K. (1983). Diagnostic Checking ARMA Time Series Models using Squared-Residual Autocorrelations. Journal of Time Series Analysis, 4, 269-273.

Monge, M. (2021). US Historical Initial Jobless Claims. Is It Different with The Coronavirus Crisis? A Fractional Integration Analysis. International Economics, 167(2021), 88-95. https://doi.org/10.1016/j.inteco.2020.11.006.

Nor, M. E., Saharan, S., Lin, L. S., Salleh, R. M., & Asrah, N. M. (2018). Forecasting of Unemployment Rate in Malaysia Using Exponential Smoothing Methods. International Journal of Engineering and Technology, 7(4.30), 451.

Peerajit, W., Areepong, Y., & Sukparungsee, S. (2018). Numerical Integral Equation Method for ARL of CUSUM Chart for Long-Memory Process with Non-Seasonal and Seasonal ARFIMA Models. Thailand Statistician, 16(1), 26-37.

Perone, G. (2020). An ARIMA Model to Forecast the Spread and The Final Size of COVID2019 Epidemic in Italy. HEDG-Health Econometrics and Data Group Working Paper Series. University of York.

Ramli, S. F., Firdaus, M., Uzair, H., Khairi, M., & Zharif, A. (2018). Prediction of The Unemployment Rate in Malaysia. International Journal of Modern Trends in Social Sciences, 1, 38-44.

Shaadan, N., Rusdi, M. S., Nik Mohd Azmi, N. N. S., Talib, S. F., & Wan Azmi, W. A. (2019). Time series Model for Carbon Monoxide (CO) At Several Industrial Sites in Peninsular Malaysia. Malaysian Journal of Computing, 4(1), 246-260.

Tan, R., (2021). Risk of Rising Unemployment Remains High with MCO 2.0. The Star Online. Retrieved on 13 Jan, 2021, from. www.thestar.com.my/business/businessnews/2021/01/13/risk-of-rising-unemployment-remains-high-with-mco-20.

Tule, M. K., Oduh, M. O., Chiemeke, C. C., & Ndukwe, O. C. (2018). An Assessment of The Severity of Unemployment in Nigeria: Evidence from Fractional Integration. Journal of African Business, 19(1), 39-61. https://doi.org/10.1080/15228916.2017.1343031.

World Health Organization Official Portal. (2020). Coronavirus. Retrieve from https://www.who.int/health-topics/coronavirus.

Downloads

Published

2022-02-14

How to Cite

Ismail, N. A. ., Ramzi, N. A. ., & Mah, P. J. W. . (2022). FORECASTING THE UNEMPLOYMENT RATE IN MALAYSIA DURING COVID-19 PANDEMIC USING ARIMA AND ARFIMA MODELS. Malaysian Journal of Computing, 7(1), 982–994. https://doi.org/10.24191/mjoc.v7i1.14641