COMPARATIVE PERFORMANCE OF ARIMA, SARIMA AND GARCH MODELS IN MODELLING AND FORECASTING UNEMPLOYMENT AMONG ASEAN-5 COUNTRIES
Abstract
Unemployment, especially after the COVID-19 pandemic, is a critical issue for any country as it has economic and social ramifications. Consequently, forecasting unemployment becomes an essential task as it can guide government policy. Time series data are frequently influenced by outliers (unexpected events), and some outliers may exist with extreme observation to reduce the forecasting effectiveness of robust estimators. This study compared the performance of Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA) and Generalised Autoregressive Conditional Heteroscedasticity (GARCH) models in modelling and forecasting unemployment rates during the COVID-19 pandemic among the ASEAN-5 countries. These countries include Malaysia, Singapore, Thailand, the Philippines and Indonesia. The monthly unemployment data from January 2010 to December 2021 were applied for all cases, except Thailand, until December 2020. Each adequate model from both forecasting mechanisms underwent forecasting. Their performance was compared based on root mean squared error (RMSE), mean absolute error (MAE), Theil inequality coefficient and symmetric mean absolute percentage error (SMAPE). Static forecasting from the ARIMA and SARIMA models was found to perform better than the GARCH model in modelling and forecasting the unemployment rate among ASEAN-5 countries during the pandemic period.
References
-day movement control order begins nationwide on Wednesday. (2020, March 16). New Straits Times. https://www.nst.com.my/news/nation/2020/03/575180/14-day-movement-control-order-begins-nationwide-wednesday
Ab Aziz, N., Mohd Shafie, S. N., & Azam Nafi, M. N. (2023). Comparative performance of ARIMA and GARCH models in modelling and forecasting volatility of Kuala Lumpur composite index. International Journal of Academic Research in Accounting, Finance and Management Sciences, 13(1), 330-343. https://doi.org/10.6007/ijarafms/v13-i1/16213
https://doi.org/10.6007/IJARAFMS/v13-i1/16213
Angco, R. J. N., Timtim, L. D., Ando, M. P., Leyson, C. L., & Villasin, C. R. P. (2021). Time series approach on Philippines' three economic participation using ARIMA model. Technium Social Sciences Journal, 25, 304-332.
Ayik, U., & Erkal, G. (2021). Forecasting of unemployment and economic growth for Turkey: ARIMA model application. Turkish Journal of Forecasting, 05(1), 12-22. https://doi.org/10.34110/forecasting.917300
https://doi.org/10.34110/forecasting.917300
Azimi, M. N., & Shahidzada, S. F. (2019). A correcting note on forecasting conditional variance using ARIMA vs. GARCH model. International Journal of Economics and Finance, 11(5), 145. https://doi.org/10.5539/ijef.v11n5p145
https://doi.org/10.5539/ijef.v11n5p145
Bank Indonesia. (2022). Unemployment rate Q1 2011- Q4 2020. Bank Indonesia. Retrieved October 1, 2022, from https://www.bi.go.id/id/statistik/sdds/Default.aspx
Bank Negara Malaysia. (2022). Unemployment rate Q1 2011- Q4 2020. Bank Negara Malaysia. Retrieved October 1, 2022, from https://www.bnm.gov.my/national-summary-data-page-for-malaysia
Bank of Thailand. (2022). Unemployment rate Q1 2011-Q4 2020. Bank of Thailand. Retrieved October 1, 2022, from https://www.bot.or.th/App/BTWS_STAT/statistics/ReportPage.aspx?reportID=638&language=eng
Box, G. E. P., & Jenkins, G. M. (1976). Time series analysis: Forecasting and control (revised edition). San Francisco: Holden Day.
Consulate General of the Republic of Indonesia. (2021, March 17). Indonesia travel restrictions. Consulate General of the Republic of Indonesia. https://kemlu.go.id/losangeles/en/news/11727/update-indonesia-travel-restrictions
Cuestas, J. C., Ordóñez, J., & Monfort, M. (2021). Measuring the cost of Covid-19 in terms of the rise in the unemployment rate: The case of Spain. Economics, 15(1), 112-114. https://doi.org/10.1515/econ-2021-0009
https://doi.org/10.1515/econ-2021-0009
Dabros, M. S., Parker, S. L., & Petersen, M. W. (2015). Assessing the stability of trust in government across election periods. Social Science Quarterly, 96(4), 996-1011. https://doi.org/10.1111/ssqu.12156
https://doi.org/10.1111/ssqu.12156
Davidescu, A. A., Apostu, S. A., & Paul, A. (2021). Comparative analysis of different univariate forecasting methods in modelling and predicting the Romanian unemployment rate for the period 2021-2022. Entropy, 23(3), 1-32. https://doi.org/10.3390/e23030325
https://doi.org/10.3390/e23030325
Dechsupa, S., Assawakosri, S., Phakham, S., & Honsawek, S. (2020). Positive impact of lockdown on COVID-19 outbreak in Thailand. Travel Medicine and Infectious Disease, 36(101802), 1-2. https://doi.org/10.1016/j.tmaid.2020.101802
https://doi.org/10.1016/j.tmaid.2020.101802
Dritsaki, C. & Dritsaki, M. (2021). Forecasting Greek real GDP based on ARIMA modeling. In Modeling Economic Growth in Contemporary Greece (Entrepreneurship and Global Economic Growth) (pp. 45-60). Emerald Publishing Limited. https://doi.org/https://doi.org/10.1108/978-1-80071-122-820211005
https://doi.org/10.1108/978-1-80071-122-820211005
Dritsaki, C. (2016). Forecast of SARIMA models: Αn application to unemployment rates of Greece. American Journal of Applied Mathematics and Statistics, 4(5), 136-148. https://doi.org/10.12691/ajams-4-5-1
Dritsakis, N., & Klazoglou, P. (2018). Forecasting unemployment Rates in USA using Box-Jenkins methodology. International Journal of Economics and Financial Issues, 8(1), 9-20.
Habibullah, M. S., Evan, L., Din, B. H., Abd Rahman, M. D., & Iskandar Shah, M. A. (2022). Long-run and short-run relationships between Covid-19 and the loss of employment in Malaysia: Evidence using GARCH-M, EGARCH-M and PGARCH-M models. Revista Portuguesa de Estudos Regionais, 60, 9-31.
https://doi.org/10.59072/rper.vi60.338
Haque, M. I., & Shaik, A. R. (2021). Predicting crude oil prices during a pandemic: A comparison of ARIMA and GARCH models. Montenegrin Journal of Economics, 17(1), 197-207. https://doi.org/10.14254/1800-5845/2021.17-1.15
https://doi.org/10.14254/1800-5845/2021.17-1.15
Haque, T. H., & Haque, M. O. (2018). The swine flu and its impacts on tourism in Brunei. Journal of Hospitality and Tourism Management, 36, 92-101.
https://doi.org/10.1016/j.jhtm.2016.12.003
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. https://doi.org/10.24191/mjoc.v7i1.14641
https://doi.org/10.24191/mjoc.v7i1.14641
Jakarta to impose partial lockdown on April 10. (2020, April 8). The Star. https://www.thestar.com.my/news/regional/2020/04/08/jakarta-to-impose-partial-lockdown-on-april-10
Katris, C. (2020). Prediction of unemployment rates with time series and machine learning techniques. Computational Economics, 55(2), 673-706. https://doi.org/10.1007/s10614-019-09908-9
https://doi.org/10.1007/s10614-019-09908-9
Khan, M., Kayani, U. N., Khan, M., Mughal, K. S., & Haseeb, M. (2023). COVID-19 pandemic & financial market volatility; Evidence from GARCH models. Journal of Risk and Financial Management, 16(1), 50. https://doi.org/10.3390/jrfm16010050
https://doi.org/10.3390/jrfm16010050
Lai, H., Khan, Y. A., Thaljaoui, A., Chammam, W., & Abbas, S. Z. (2021). COVID-19 pandemic and unemployment rate: A hybrid unemployment rate prediction approach for developed and developing countries of Asia. Soft Computing, 1-16. https://doi.org/10.1007/s00500-021-05871-6
https://doi.org/10.1007/s00500-021-05871-6
Lip, N. M., Lina, N., Rizuan, N. M., Iezudin, N. I., Mohamad, N. A., Rasyida, N., Rasyid, M., Hassan, F. A., & Ithnin, H. (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.
Mahipan, K., Chutiman, N., & Kumphon, B. (2013). A forecasting model for Thailand's unemployment rate. Modern Applied Science, 7(7), 10-16. https://doi.org/10.5539/mas.v7n7p10
https://doi.org/10.5539/mas.v7n7p10
Mahmudah, U. (2017). Predicting unemployment rates in Indonesia. Economic Journal of Emerging Markets, 9(1), 20-28. https://doi.org/10.20885/ejem.vol9.iss1.art3
https://doi.org/10.20885/ejem.vol9.iss1.art3
Martin, A., Mikołajczak, G., Baekkeskov, E., & Hartley, K. (2022). Political stability, trust and support for public policies: A survey experiment examining source effects for COVID-19 interventions in Australia and Hong Kong. International Journal of Public Opinion Research, 34(3), 1-10. https://doi.org/10.1093/ijpor/edac024 Metro Manila to be placed on "lockdown" due to COVID-19. (2020, March 12). CNN Philippines. https://www.cnnphilippines.com/news/2020/3/12/COVID-19-Metro-Manila-restrictions-Philippines.html
https://doi.org/10.1093/ijpor/edac024
Meyler, Aidan, Kenny, Geoff, Quinn, & Terry. (1998). Forecasting Irish inflation using ARIMA models. Munich Personal RePEc Archive, 11359, 1-8.
Ministry of Foreign Affairs Malaysia. (2022, March 31). Malaysian borders reopening beginning 1st April 2022. Ministry of Foreign Affairs Malaysia. https://www.kln.gov.my/web/aus_canberra/news-from-mission/-/blogs/announcement-malaysian-border-reopening-beginning-1st-april-2022
Ministry of Manpower Singapore. (2022). Unemployment rate Q1 2011- Q4 2020. Ministry of Manpower Singapore. Retrieved October 1, 2022, from https://stats.mom.gov.sg/Pages/Unemployment-Summary-Table.aspx
Miswan, N. H., Ngatiman, N. A., Hamzah, K., & Zamzamin, Z. Z. (2014). Comparative performance of ARIMA and GARCH models in modelling and forecasting volatility of Malaysia market properties and shares. Applied Mathematical Sciences, 8(137-140), 7001-7012. https://doi.org/10.12988/ams.2014.47548
https://doi.org/10.12988/ams.2014.47548
Muğaloğlu, E., & Kiliç, E. (2021). G7 countries unemployment rate predictions using seasonal ARIMA-GARCH coupled models. Journal of Yasar University, 16(61), 228-247.
https://doi.org/10.19168/jyasar.803807
Nguyen, P. H., Tsai, J. F., Kayral, I. E., & Lin, M. H. (2021). Unemployment rates forecasting with grey-based models in the post-COVID-19 period: A case study from Vietnam. Sustainability, 13(14), 7879. https://doi.org/10.3390/su13147879
https://doi.org/10.3390/su13147879
Nkoane, S. S., & Seeletse, S. M. (2021). Forecasting unemployment rate in South Africa with unexpected events using robust estimators. International Journal of Economics and Finance Studies, 13(2), 199-222. https://doi.org/10.34109/ijefs.20212010
Nuryatin, A. (2020). Comparative analysis of ARIMA and GARCH methods to predict stock prices. Almana: Jurnal Manajemen dan Bisnis, 4(3), 405-415. https://doi.org/10.36555/almana.v4i3.1483
https://doi.org/10.36555/almana.v4i3.1483
Philippines Stastitics Authority. (2022). Unemployment rate Q1 2011- Q4 2020. Philippines Stastitics Authority. Retrieved October 1, 2022, from https://psa.gov.ph/.
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(4), 38-44.
Singapore to see most workplaces closed down from Tuesday. (2020, April 3). The Star. https://www.thestar.com.my/news/regional/2020/04/03/singapore-pm-we-will-put-in-place-a-circuit-breaker-to-pre-empt-escalating-coronavirus-infections
Sójka, B. B. (2017). Unemployment rates forecasts - Unobserved component models versus SARIMA models. De Gruytere, 20(2), 91-107. https://doi.org/https://doi.org/10.1515/cer-2017-0014
https://doi.org/10.1515/cer-2017-0014
Stoklasova, R. (2012, September). Model of the unemployment rate in the Czech Republic. In Proceedings of 30th International Conference on Mathematical Methods in Economics, Karviná, Czech Republic, 11-13 September, 2012. (pp. 836-841). http://www.sciepub.com/reference/171581
Sullivan, B. (2022, May 3). Southeast Asia Covid-19: Shifting from pandemic to endemic. Thailand Business News. https://www.thailand-business-news.com/health/88837-southeast-asia-covid-19-shifting-from-pandemic-to-endemic Thailand drops post-arrival Covid-19 quarantine, opens all land borders. (2022, June 1). New Straits Times. https://www.nst.com.my/world/world/2022/06/801447/thailand-drops-post-arrival-covid-19-quarantine-opens-all-land-borders
Tong, G. C. (2022, March 25). Barclays expects 'big jump' in Singapore growth after Covid measures are lifted. CNBC. https://www.cnbc.com/2022/03/25/singapore-economy-barclays-expects-gdp-growth-after-lifting-of-covid-measures.html
Tufaner, M. B., & Sözen, İ. (2021). Forecasting unemployment rate in the aftermath of the Covid-19 pandemic: The Turkish case. İzmir İktisat Dergisi, 0-3. https://doi.org/10.24988/ije.202136312
https://doi.org/10.24988/ije.202136312
Urrutia, J. D., Tampis, R. L., & Atienza, J. E. (2017). An analysis on the unemployment rate in the Philippines: A time series data approach. In Journal of Physics: Conference Series, 820(1). https://doi.org/10.1088/1742-6596/755/1/011001
https://doi.org/10.1088/1742-6596/755/1/011001
Uwilingiyimana, C., Munga'Tu, J., & Harerimana, J. D. D. (2016). Forecasting inflation in Kenya using ARIMA - GARCH models. International Journal of Management and Commerce Innovations, 3(2), 15-27.
Verma, S. (2021). Forecasting volatility of crude oil futures using a GARCH-RNN hybrid approach. Intelligent Systems in Accounting, Finance and Management, 28(2), 130-142. https://doi.org/10.1002/isaf.1489
https://doi.org/10.1002/isaf.1489
Waffa, B., & Wahiba, H. (2022). Forecasting Algeria's unemployment rates using SARIMA model in Python programming: During 2001-2021. Forum For Economic Studies and Research Journal, 6(1), 586-600.
Yunita, I. (2016). Volatility modeling using ARCH/GARCH method: Aplication on Asia Pasific Index. In 3rd International Seminar and Conference on Learning Organization (pp.182 - 189). Atlantic Press. https://doi.org/10.2991/isclo-15.2016.34
https://doi.org/10.2991/isclo-15.2016.34
Zhou, Z., Fu, Z., Jiang, Y., Zeng, X., & Lin, L. (2020). Can economic policy uncertainty predict exchange rate volatility? New evidence from the GARCH-MIDAS model. Finance Research Letters, 34(May 2019), 101258. https://doi.org/10.1016/j.frl.2019.08.006
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