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COVID-19 pandemic and unemployment rate: A hybrid unemployment rate prediction approach for developed and developing countries of Asia

Authors
  • Lai, Han1
  • Khan, Yousaf Ali2, 3
  • Thaljaoui, Adel4
  • Chammam, Wathek4
  • Abbas, Syed Zaheer2
  • 1 Huanghuai University,
  • 2 Hazara University Mansehra,
  • 3 Jiangxi University of Finance and Economics,
  • 4 Majmaah University,
Type
Published Article
Journal
Soft Computing
Publisher
Springer-Verlag
Publication Date
May 19, 2021
Pages
1–16
Identifiers
DOI: 10.1007/s00500-021-05871-6
PMID: 34025212
PMCID: PMC8132284
Source
PubMed Central
Keywords
Disciplines
  • Focus
License
Unknown

Abstract

Unemployment remains a serious issue for both developed and developing countries and a driving force to lose their monetary and financial impact. The estimation of the unemployment rate has drawn researchers' attention in recent years. This investigation's key objective is to inquire about the impact of COVID-19 on the unemployment rate in selected, developed and developing countries of Asia. For experts and policymakers, effective prediction of the unemployment rate is an influential test that assumes an important role in planning the monetary and financial development of a country. Numerous researchers have recently utilized conventional analysis tools for unemployment rate prediction. Notably, unemployment data sets are nonstationary. Therefore, modeling these time series by conventional methods can produce an arbitrary mistake. To overcome the accuracy problem associated with conventional approaches, this investigation assumes intelligent-based prediction approaches to deal with the unemployment data and to predict the unemployment rate for the upcoming years more precisely. These intelligent-based unemployment rate strategies will force their implications by repeating diversity in the unemployment rate. For illustration purposes, unemployment data sets of five advanced and five developing countries of Asia, essentially Japan, South Korea, Malaysia, Singapore, Hong Kong, and five agricultural countries (i.e., Pakistan, China, India, Bangladesh and Indonesia) are selected. The hybrid ARIMA-ARNN model performed well among all hybrid models for advanced countries of Asia, while the hybrid ARIMA-ANN outperformed for developing countries aside from China, and hybrid ARIMA-SVM performed well for China. Furthermore, for future unemployment rate prediction, these selected models are utilized. The result displays that in developing countries of Asia, the unemployment rate will be three times higher as compared to advanced countries in the coming years, and it will take double the time to address the impacts of Coronavirus in developing countries than in developed countries of Asia.

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