Modelamiento y predicción del efecto COVID-19 en el sistema laboral ecuatoriano
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El COVID-19 ha causado una interrupción masiva en diferentes niveles. Académicos de todo el mundo han producido una cantidad significativa de estudios para comprender, reducir y predecir los efectos de esta pandemia. Los modelos de predicción son cruciales en este momento de incertidumbre y los indicadores laborales son variables macroeconómicas clave para planificar la recuperación de los efectos del COVID-19. Este estudio tiene como objetivo modelar y predecir la tendencia del sistema laboral ecuatoriano. El modelo de análisis estadístico aplicado para este estudio fue el X-13ARIMA para predecir el comportamiento de cuatro indicadores del sistema laboral ecuatoriano y establecer su tendencia natural aislando la variable COVID-19 y determinar el impacto cuantitativo de la pandemia. Los resultados muestran que el sistema laboral se vio muy afectado por el brote de COVID-19; sin embargo, ya se observaba un deterioro en el pleno empleo y el subempleo ampliado. El estudio concluye que la pandemia alteró la estacionalidad de los indicadores laborales.
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Aditya Satrio, C. B., Darmawan, W., Nadia, B. U., & Hanafiah, N. (2021). Time series analysis and forecasting of coronavirus disease in Indonesia using ARIMA model and PROPHET. Procedia Computer Science, 179, 524–532. https://doi.org/10.1016/J.PROCS.2021.01.036
Alsharif, M. H., Younes, M. K., & Kim, J. (2019). Time Series ARIMA Model for Prediction of Daily and Monthly Average Global Solar Radiation: The Case Study of Seoul, South Korea. Symmetry 2019, Vol. 11, Page 240, 11(2), 240. https://doi.org/10.3390/SYM11020240
Alshater, M. M., Almansour, A. Y., & Almansour, B. Y. (2021). Performance of ARCH and GARCH Models in Forecasting Cryptocurrency Market Volatility. Industrial Engineering & Management Systems, 20(2), 130–139. https://doi.org/10.7232/iems.2021.20.2.130
Asmelash Gebretensae, Y., & Asmelash, D. (2021). Trend Analysis and Forecasting the Spread of COVID-19 Pandemic in Ethiopia Using Box-Jenkins Modeling Procedure. International Journal of General Medicine, 14, 1485. https://doi.org/https://doi.org/10.2147%2FIJGM.S306250
Benanav, A. (2019). The origins of informality: the ILO at the limit of the concept of unemployment. Journal of Global History, 14(1), 107–125. https://doi.org/https://doi.org/10.1017/S1740022818000372
Benvenuto, D., Giovanetti, M., Vassallo, L., Angeletti, S., & Ciccozzi, M. (2020). Application of the ARIMA model on the COVID-2019 epidemic dataset. Data in Brief, 29, 105340. https://doi.org/10.1016/J.DIB.2020.105340
Bherwani, H., Gautam, S., & Gupta, A. (2021). Qualitative and quantitative analyses of impact of COVID-19 on sustainable development goals (SDGs) in Indian subcontinent with a focus on air quality. International Journal of Environmental Science and Technology 2021 18:4, 18(4), 1019–1028. https://doi.org/10.1007/S13762-020-03122-Z
Bógalo, J., Llada, M., Poncela, P., & Senra, E. (2022). Seasonality in COVID-19 times. Economics Letters, 211, 110206. https://doi.org/10.1016/J.ECONLET.2021.110206
Chung, R. C. P., Ip, W. H., & Chan, S. L. (2009). An ARIMA-intervention analysis model for the financial crisis in China’s manufacturing industry. International Journal of Engineering Business Management, 1(1), 15–18. https://doi.org/10.5772/6785
ENEMDU. (2019). National Survey for Employment, Unemployment and Underemployment. https://www.ecuadorencifras.gob.ec/enemdu-diciembre-2019/
European Union, C. (2017). Release of JDemetra+ version 2.2 as software officially recommended for the seasonal and calendar adjustment of official statistics in the EU. Collaboration in Research and Methodology for Official Statistics. https://bit.ly/3tlLQhc
Fattah, J., Ezzine, L., Aman, Z., Moussami, H. El, & Lachhab, A. (2018). Forecasting of demand using ARIMA model: Https://Doi.Org/10.1177/1847979018808673, 10. https://doi.org/10.1177/1847979018808673
Goel, R. K., Saunoris, J. W., & Goel, S. S. (2021). Supply chain performance and economic growth: The impact of COVID-19 disruptions. Journal of Policy Modeling, 43(2), 298–316. https://doi.org/10.1016/J.JPOLMOD.2021.01.003
Gómez, V., Maravall, A., & Peña, D. (1999). Missing observations in ARIMA models: Skipping approach versus additive outlier approach. Journal of Econometrics, 88(2), 341–363. https://doi.org/10.1016/S0304-4076(98)00036-0
Guleryuz, D. (2021). Forecasting outbreak of COVID-19 in Turkey; Comparison of Box–Jenkins, Brown’s exponential smoothing and long short-term memory models. Process Safety and Environmental Protection, 149, 927–935. https://doi.org/10.1016/J.PSEP.2021.03.032
Hernandez-Matamoros, A., Fujita, H., Hayashi, T., & Perez-Meana, H. (2020). Forecasting of COVID19 per regions using ARIMA models and polynomial functions. Applied Soft Computing, 96, 106610. https://doi.org/10.1016/J.ASOC.2020.106610
Ho, S. L., & Xie, M. (1998). The use of ARIMA models for reliability forecasting and analysis. Computers & Industrial Engineering, 35(1–2), 213–216. https://doi.org/https://doi.org/10.1016/S0360-8352(98)00066-7
Hyndman, R. J., & Khandakar, Y. (2008). Automatic Time Series Forecasting: The forecast Package for R. Journal of Statistical Software, 27(3), 1–22. https://doi.org/10.18637/JSS.V027.I03
Khamis, M., Prinz, D., Newhouse, D., Palacios-Lopez, A., Pape, U., & Weber, M. (2021). The Early Labor Market Impacts of COVID-19 in Developing Countries : Evidence from High-Frequency Phone Surveys. Working Paper;No. 58. World Bank, Washington, DC. © World Bank. https://bit.ly/3QdVFr9
Kim, S., Choi, C.-Y., Shahandashti, M., & Ryu, K. R. (2021). Improving Accuracy in Predicting City-Level Construction Cost Indices by Combining Linear ARIMA and Nonlinear ANNs. Journal of Management in Engineering, 38(2), 04021093. https://doi.org/10.1061/(ASCE)ME.1943-5479.0001008
Lemieux, T., Milligan, K., Schirle, T., & Skuterud, M. (2020). Initial Impacts of the COVID-19 Pandemic on the Canadian Labour Market. Https://Doi.Org/10.3138/Cpp.2020-049, 46(1), S55–S65. https://doi.org/10.3138/CPP.2020-049
Lim, W. M., & Wai-Ming, T. (2022). The economic impact of a global pandemic on the tourism economy: The case of COVID-19 and Macao´s destination-and-gambling-dependent. Current Issues in Tourism, 25(8), 1258–1269. https://doi.org/https://doi.org/10.1080/13683500.2021.1910218
Makridakis, S., & Hibon, M. (1997). ARMA models and the Box-Jenkins methodology. Journal of Forecasting, 16(3), 147–163. https://doi.org/https://doi.org/10.1002/(SICI)1099-131X(199705)16:3%3C147::AID-FOR652%3E3.0.CO;2-X
McBurney, M., Tuaza, L. A., Ayol, C., & Johnson, C. A. (2021). Land and livelihood in the age of COVID-19: Implications for indigenous food producers in Ecuador. Journal of Agrarian Change, 21(3), 620–628. https://doi.org/10.1111/JOAC.12417
Mueller, J. T., McConnell, K., Burow, P. B., Pofahl, K., Merdjanoff, A. A., & Farrell, J. (2021). Impacts of the COVID-19 pandemic on rural America. Proceedings of the National Academy of Sciences, 118(1). https://doi.org/10.1073/PNAS.2019378118
Nadal Rosselló, J., Font Riera, A., & Sansó Rosselló, A. (2004). The economic determinants of seasonal patterns. Annals of Tourism Research, 31(3), 697–711. https://doi.org/https://doi.org/10.1016/j.annals.2004.02.001
Naylor, T. H., Seaks, T. G., & Wichem, D. W. (1972). Box-Jenkins methods: An alternative to econometrics models. International Statistical Review/Revue Internationale de Statisque, 123–137. https://doi.org/https://doi.org/10.2307/1402755
Newbold, P. (1983). ARIMA model building and the time series analysis approach to forecasting. Journal of Forecasting, 2(1), 23–35. https://doi.org/https://doi.org/10.1002/for.3980020104
Parino, F., Zino, L., Porfiri, M., & Rizzo, A. (2021). Modelling and predicting the effect of social distancing and travel restrictions on COVID-19 spreading. Journal of the Royal Society Interface, 18(175). https://doi.org/10.1098/RSIF.2020.0875
Rosén, M., & Stenbeck, M. (2020). Interventions to suppress the coronavirus pandemic will increase unemployment and lead to many premature deaths: Scandinavian Journal of Public Health. https://doi.org/10.1177/1403494820947974
Sagaert, Y. R., Aghezzaf, E. H., Kourentzes, N., & Desmet, B. (2018). Tactical sales forecasting using a very large set of macroeconomic indicators. European Journal of Operational Research, 264(2), 558–569. https://doi.org/10.1016/J.EJOR.2017.06.054
Sakutukwa, T., & Yang, H. S. (2018). The role of uncertainty in forecasting employment by skill and industry. Applied Economics Letters, 25(18), 1288–1291. https://doi.org/https://doi.org/10.1080/13504851.2017.1418069
Sánchez, M., Ochoa M, W. S., Toledo, E., & Ordóñez, J. (2020). The relevance of Index of Sustainable Economic Wellbeing. Case study of Ecuador. Environmental and Sustainability Indicators, 6, 100037. https://doi.org/10.1016/J.INDIC.2020.100037
Sarkodie, S. A., Phebe, ·, Owusu, A., Owusu, P. A., Sarkodie, S. A., & Owusu, P. A. (2021). Global assessment of environment, health and economic impact of the novel coronavirus (COVID-19) Tunisian dinar OECD Organisation for economic co-operation and development RM Malaysian Ringgit NOK Norwegian Krone SARS Severe acute respiratory syndrome-rel. Environment, Development and Sustainability, 23, 5005–5015. https://doi.org/10.1007/s10668-020-00801-2
Siami-Namini, S., & Namin, A. S. (2018). Forecasting Economics and Financial Time Series: ARIMA vs. LSTM. ArXiv Preprint ArXiv:1803.06386. https://doi.org/https://doi.org/10.48550/arXiv.1803.06386
Siami-Namini, S., Tavakoli, N., & Siami Namin, A. (2019). A Comparison of ARIMA and LSTM in Forecasting Time Series. Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018, 1394–1401. https://doi.org/10.1109/ICMLA.2018.00227
Sowell, F. (1992). Modeling long-run behavior with the fractional ARIMA model. Journal of Monetary Economics, 29(2), 277–302. https://doi.org/10.1016/0304-3932(92)90016-U
Taneja, K., Ahmad, S., Ahmad, K., & Attri, S. (2016). Time series analysis of aerosol optical depth over New Delhi using Box-Jenkins ARIMA modeling approach. Atmospheric Pollution Research, 7(4), 585–596. https://doi.org/https://doi.org/10.1016/j.apr.2016.02.004
Tseng, F. M., & Tzeng, G. H. (2002). A fuzzy seasonal ARIMA model for forecasting. Fuzzy Sets and Systems, 126(3), 367–376. https://doi.org/10.1016/S0165-0114(01)00047-1
UNECE. (2020). Homepage | UNECE. United Nations. https://unece.org/
Wilke, R. A. (2018). Forecasting Macroeconomic Labour Market Flows: What Can We Learn from Micro-level Analysis? Oxford Bulletin of Economics and Statistics, 80(4), 822–842. https://doi.org/https://doi.org/10.1111/obes.12222
Wolff, J. (2018). Ecuador after Correa: the struggle over the “citizens’’ revolution".” Revista de Ciencia Política, 38(2), 281–302.
World Bank. (2021). Ecuador Overview: Development news, research, data | World Bank. World Bank Publications. https://bit.ly/3xs2SNb