Home⇛PUP Journal of Science and Technology⇛vol. 14 no. 1 (2021)

FORECASTING THE IMPACT OF COVID-19 ON THE HOUSEHOLD FINAL CONSUMPTION EXPENDITURE (HFCE) IN THE PHILIPPINES

Andre Perry P Dasmariñas | Gweneth H De Castro | Bea Jane M Lazona | Laurence P Usona

Discipline: Statistics

 

Abstract:

The Household Final Consumption Expenditure (HFCE) is a significant component of the Philippine economy. The term "HFCE" refers to families' spending on necessities such as food and drink, clothing, shelter, and health care. The gathered data from this study covers the country’s quarterly HFCE from 2001-2021. The study used various forecasting methods, including Triple Exponential Smoothing, Seasonal Autoregressive Integrated Moving Average (SARIMA), and TBATS in modeling time series data to depict the effects of COVID-19 on the country’s HFCE Growth Rate. The best model to predict the quarterly HFCE growth rate from 2022 to 2026 was identified using error metrics, particularly RMSE, MSE, and MAE. The SARIMA model had the lowest combined error of the train and test set, marking it as the best model for predicting the HFCE growth rate. Moreover, the HFCE growth rate was also predicted using various machine learning regression algorithms (SVR, XGBoost, and kNN), with a variety of economic indicators as the independent variables, including the inflation rate, unemployment rate, import of goods growth rate, import of services growth rate, export of goods growth rate, and export of services growth rates. The results of error metrics showed that SVR was the best regression algorithm for predicting the Philippines’ quarterly HFCE Growth Rate. The findings of this study indicate that as COVID-19 spread over the country, the HFCE growth rate dramatically decreased. Therefore, models used to predict the HFCE growth rate over the next five years are significantly impacted using historical data, including the years when COVID-19 occurred.



References:

  1. Arapova, E. (2018). Determinants of household final consumption expenditures in Asian countries: A panel model, 1991–2015. Applied Econometrics and International Development, 18(1), 121-140.
  2. Bajaj, A. (2022). ARIMA & SARIMA: Real-World Time Series Forecasting. Neptune.Ai. https://neptune.ai/blog/arima-sarima-real-world-time-seriesforecasting-guide
  3. Biswas, R. (2021). Philippines Economic Rebound Hit by New COVID-19 Wave. Escalating New COVID-19 Cases Dampens Recovery. https://ihsmarkit.com/research-analysis/Philippine, https://ihsmarkits-economic-rebound-hit-by-new-covid19wave.html.com/research-analysis/Philippines-economic-rebound-hit-bynew-covid19-wave.html
  4. Blaconá, M.T, Andreozzi, L. and Magnano, L. (2014). Time series models for different seasonal patterns. Retrieved from https://forecasters.org/wp-content/uploads/gravity_forms/72a51b93047891f1ec3608bdbd77ca58d/2014/06/Blacon%C3%A1_MT_ISF2014.pdf.pdf
  5. Brownlee, J. (2019, August 21). A Gentle Introduction to SARIMA for Time Series Forecasting in Python.Machine Learning Mastery. https://machinelearningmastery.com/sarima-for-time-series-forecasting-in-python/
  6. Brownlee, J. (2021, March 6). XGBoost for Regression.Machine Learning Mastery. https://machinelearningmastery.com/xgboost-for-regression/
  7. Chatterjee, S. (2018, January 30). Time series analysis using Arima model in R. DataScience+. Retrieved July 25, 2022, from https://datascienceplus.com/timeseries-analysis-using-arima-model-in-r/
  8. Dela Cruz, A. (2019). Forecasting Philippine household final consumption expenditure on education using discrete wavelet transformation on hybrid ARIMA-ANN model. Indian Journal of Science and Technology, 12(33). https://doi.org/10.17485/ijst/2019/v12i33/146427 
  9. Erero, J. &Makananisa M. (2020). Impact of Covid-19 on the South African economy: A CGE, Holt-Winter and SARIMA model’s analysis. Turkish Economic Review, 7(4). Retrieved http://www.kspjournals.org/index.php/TER/article/view/2129 from Frost, J. (2021, May 18). Exponential Smoothing for Time Series Forecasting. Statistics By Jim. https://statisticsbyjim.com/time-series/exponential-smoothingtime-series-forecasting/
  10. Fürnkranz, J., Chan, P. K., Craw, S., Sammut, C., Uther, W., Ratnaparkhi, A., Jin, X., Han, J., Yang, Y., Morik, K., Dorigo, M., Birattari, M., Stützle, T., Brazdil, P., Vilalta, R., Giraud-Carrier, C., Soares, C., Rissanen, J., Baxter, R. A., . . . De Raedt, L. (2011). Mean Squared Error. Encyclopedia of Machine Learning, 653. https://doi.org/10.1007/978-0-387-30164-8_528
  11. Fürnkranz, J., Chan, P. K., Craw, S., Sammut, C., Uther, W., Ratnaparkhi, A., Jin, X., Han, J., Yang, Y., Morik, K., Dorigo, M., Birattari, M., Stützle, T., Brazdil, P., Vilalta, R., Giraud-Carrier, C., Soares, C., Rissanen, J., Baxter, R. A., . . . De Raedt, L. (2011a). Mean Absolute Error. Encyclopedia of Machine Learning, 652. https://doi.org/10.1007/978-0-387-30164-8_525 
  12. Gubangco, A. G. D., Joves, J. D. S., & Pizarro-Uy, A. C. D. (2022). Consumption in the Philippines: In the course of unemployment and loan acquisition. Journal of Economics, Finance and Accounting Studies, 4(2), 18-34.
  13. Han, R. (2022, May). What is TBATS model in time series in R How to use it .ProjectPro. https://www.projectpro.io/recipes/what-is-tbats-model-time-seriesuse-it
  14. Handriyani, R., Sahyar, M. M., & Si, A. M. (2018). Analysis the effect of household  consumption expenditure, investment and labor to economic growth: A case in province of North Sumatra. StudiaUniversitatisVasileGoldiș Arad,SeriaȘtiințeEconomice, 28(4), 45-54.
  15. Hyndman, R. J. (2018). In G. Athanasopoulos (Ed.), Forecasting: Principles and Practice (2nd ed). Otexts. https://otexts.com/fpp2/holt-winters.html
  16. Karabiber OA, Xydis G. (2019). Electricity price forecasting in the Danish Day-ahead market using the TBATS, ANN and ARIMA methods. Energies.12(5). Retrieved from https://doi.org/10.3390/en12050928
  17. Kumar, A. (2022, February 21). R-squared, R² in Linear Regression: Concepts, Examples. Data Analytics. https://vitalflux.com/r-squared-explained-machinelearning/
  18. Long, G. (2010). GDP prediction by support vector machine trained with genetic algorithm. 2010 2nd International Conference on Signal Processing Systems. https://doi.org/10.1109/icsps.2010.5555854
  19. Madhav, N., Oppenheim, B., Gallivan,M., Mulembakani, P., Rubin, E., and Wolfe, N. (2017). Pandemics: Risks, Impacts, and Mitigation. lnJamison DT, Gelband H, Horton S, et al., (Eds). Disease Control Priorities: Improving Health and Reducing Poverty (3rd ed). Washington DC.
  20. McCloskey, B. (2022). Forecasting My Future Grocery Bills. Towards Data Science. Retrieved from https://towardsdatascience.com/forecasting-my-future-grocerybills-59515b9348d3
  21. Obinna, O. (2020). Effect of inflation on household final consumption expenditure in Nigeria. Journal of Economics and Development Studies, 8(1), 104-111.
  22. Pascasio, M. C., Dimafelix, A. D., Dimafelix, J. A. F., Chavez, L. T., &Robredo, J. E. P. (n.d.). Demystifying the Household Final Consumption Expenditure (HFCE) in the Philippine System of National Accounts (PSNA). Philippine Statistics Authority. https://psa.gov.ph/sites/default/files/1.1.1%20Demystifying%20the%20Household%20Final%20Consumption%20Expenditure%20%28HFCE%29%20in%20the%20Philippine%20System%20of%20National%20Accounts%20%28PSNA%29_0.pdf
  23. Pedamkar, P. (2021, November 15). Support Vector Regression. EDUCBA. https://www.educba.com/support-vector-regression/
  24. Philippine Statistics Authority .(2017). Inflation Rate.In Official Concept and Definition. https://psa.gov.ph/ISSiP/concepts-and-definitions/161406
  25. Priambodo, B., Rahayu, S., Hazidar, A. H., Naf’an, E., Masril, M., Handriani, I., Pratama Putra, Z., KudrNseaf, A., Setiawan, D., &Jumaryadi, Y. (2019). Predicting GDP of Indonesia using K-Nearest Neighbour Regression. Journal of Physics:Conference Series, 1339(1), 012040. https://doi.org/10.1088/1742-6596/1339/1/012040
  26. Qureshi, S. ,Chu B. & Demers, F. (2020). Forecasting Canadian GDP growth using XGBoost.Carleton Economic Papers 20(14), Carleton University, Department of Economics. Retrieved from https://ideas.repec.org/p/car/carecp/20-14.html
  27. Rohmah, M. F., Putra, I. K. G. D., Hartati, R. S., &Ardiantoro, L. (2021). Comparison four kernels of SVR to predict consumer price index. Journal of Physics: Conference Series, 6596/1737/1/0120181737(1), 012018. https://doi.org/10.1088/1742
  28. SAP. (2018). SAP HANA Predictive Analysis Library (PAL). Triple Exponential Smoothing,400.https://help.sap.com/doc/86fb8d26952748debc8d08db756e6c1f/1.0.12/en-US/SAP_HANA_Predictive_Analysis_Library_PAL_en.pdf
  29. Sayad, S. (n.d.).KNN Regression.Saedsayad. https://www.saedsayad.com/k_nearest_neighbors_reg.htm
  30. Statista, Inc. (2022, January). Household final consumption expenditure on health in the Philippines from 2017 to 2021. https://www.statista.com/statistics/709061/philippines-household-co.statista.com/statistics/709061/nsumption-expenditurehealth/Philippines-household-consumption-expenditure-health/
  31. Sugiarto, S., &Wibowo, W. (2020). Determinants of regional household final consumption expenditure in Indonesia.JEJAK, 13(2), 332-344. Team, E. A. (2022, July 16). Calculating Mean Squared Error in Python. Educative: Interactive Courses for Software Developers. https://www.educative.io/answers/calculating-mean-squared-error-in-python
  32. Teixeira-Pinto, A. (2021, August 2). 2 K-nearest Neighbours Regression Machine Learning for Biostatistics.The University of Sydney. https://bookdown.org/tpinto_home/Regression-and-Classification/k-nearest-neighbours- regression.html
  33. TenthPlanet. (2020, November 23). Time-Series Forecasting using TBATS model – TenthPlanet Technologies. forecasting-tbats/ Blogs. https://blog.tenthplanet.in/time-series
  34. The World Bank Group.(2020). Impacts of COVID-19 on Households in the Philippines.Results from the Philippines COVID-19 Households Survey. https://thedocs.worldbank.org/en/doc/ab24c2a718fb53a344c5942d236b2fe60070062021/original/Philippines-COVID-19-High-Frequency-SurveyHousehold-Results-Slides.pdf
  35. The World Bank Group.(2020b, November).Monitoring COVID-19 Impacts on Families and Firms in the Philippines. https://www.worldbank.org/en/country/philippines/brief/monitoring-covid-19-impacts-on-firms-and-families-in-the-philippines
  36. Varlamova, J., &Larionova, N. (2015). Macroeconomic and demographic determinants of household expenditures in OECD countries.Procedia Economics and Finance, 24, 727-733.
  37. Yadav, A. (2018, October 22). SUPPORT VECTOR MACHINES(SVM) – Towards Data Science. Medium. https://towardsdatascience.com/support-vector-machinessvm-c9ef22815589.
  38. World Health Organization. (2020, January 10). Coronavirus. https://www.who.int/health-topics/coronavirus#tab=tab_1

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