The impact of COVID‐19 on unemployment rate: An intelligent based unemployment rate prediction in selected countries of Europe

Abstract

Unemployment remains a major cause for both developed and developing nations, due to which they lose their financial and economic impact as a whole. Unemployment rate prediction achieved researcher attention from a fast few years. The intention of doing our research is to examine the impact of the coronavirus on the unemployment rate. Accurately predicting the unemployment rate is a stimulating job for policymakers, which plays an imperative role in a country's financial and financial development planning. Classical time series models such as ARIMA models and advanced non‐linear time series methods be previously hired for unemployment rate prediction. It is known to us that mostly these data sets are non‐linear as well as non‐stationary. Consequently, a random error can be produced by a distinct time series prediction model. Our research considers hybrid prediction approaches supported by linear and non‐linear models to preserve forecast the unemployment rates much precisely. These hybrid approaches of the unemployment rate can advance their estimates by reproducing the unemployment ratio irregularity. These models' appliance is exposed to six unemployment rate statistics sets from Europe's selected countries, specifically France, Spain, Belgium, Turkey, Italy and Germany. Among these hybrid models, the hybrid ARIMA‐ARNN forecasting model performed well for France, Belgium, Turkey and Germany, whereas hybrid ARIMA‐SVM performed outclass for Spain and Italy. Furthermore, these models are used for the best future prediction. Results show that the unemployment rate will be higher in the coming years, which is the consequence of the coronavirus, and it will take at least 5 years to overcome the impact of COVID‐19 in these countries.

RETRACTED ARTICLE: COVID-19 pandemic and unemployment rate: A hybrid unemployment rate prediction approach for developed and developing countries of Asia

“…Given the difficulty of forecasting market COVID-19 pandemic and unemployment rate: A hybrid unemployment rate prediction approach for… reaction, correctly computing the unemployment rate is helpful for consumers to outflow market risk from the unexpected trade of project conditions and economical care. This research continues our earlier investigation on the effects of the Coronavirus on the unemployment rate in selected European countries (Ahmad et al 2020). Here, in this work, we consider five developed and five developing countries of Asia (i.e., Japan, Singapore, South Korea, Hong Kong, Malaysia, Pakistan, India, Bangladesh, China, and Indonesia).…”

Health Systems’ Challenges and Responses for Recovery in the Pre and Post COVID-19 Era

“…Therefore, individuals with low income face a steeper trade-off between their health and economic status, due to the threats posed by the coronavirus (Brown & Martin, 2020). A major consequence of the COVID-19 projected by Ahmad et al (2021), is that the unemployment rate will be higher in the forthcoming years, and last for more than 5 years, resulting in a decrease of the household income and the luxury consumption commodities (Celik et al, 2020;Martin et al, 2020). COVID-19 pandemic has also contributed to the increase in the prevalence of social interaction, isolation, loneliness, boredom, domestic inter-personal violence and mental health disorders in the population (Banerjee & Rai, 2020;Mukhtar, 2020).…”

Unemployment Rates Forecasting with Grey-Based Models in the Post-COVID-19 Period: A Case Study from Vietnam

“…The ARIMA model was used by Claveria [30] to examine unemployment rates in eight European Union countries between 2007 and 2017. Moreover, Ahmad et al [31] used a hybrid ARIMA-SVM-ARNN prediction methodology to forecast unemployment rates for six selected European nations, notably France, Spain, Belgium, Turkey, Italy, and Germany.…”