ABSTRACT
The main objective of this paper is to investigate the dynamic relationship between the COVID-19 infected cases and the number of deaths due to COVID-19 using the Johnsen-Fisher co-integration test, vector error correction model and Granger causality test. The daily COVID-19-infected new cases and daily deaths due to COVID-19 in the United States, Canada, Ukraine and India were collected from the website for the period from 01-04-2020 to 26-12-2020. The summary statistics revealed that the highest numbers of COVID-19-infected cases were registered in the United States, followed by India, Canada and Ukraine;the highest numbers of deaths due to COVID-19 were registered in the United States, followed by India, Ukraine and Canada. The death percentage is exceedingly high in Canada, followed by the United States, Ukraine and India. The Johnsen-Fisher co-integration test results reveal the existence of one co-integration equation. The vector error correction model and Granger causality test reveal that long-term and short-term causality exists between COVID-19 infection and death cases. The speed of adjustment is found to be 9.9%. © 2022 NSP Natural Sciences Publishing Cor.
ABSTRACT
The primary objectives of the present study are to investigate the short- and long-term cointegration relationships between the cumulative number of new cases of COVID-19 infections (X) and the cumulative numbers of deaths due to COVID-19 (Y), to investigate the long-run equilibrium relationship between these variables using an autoregressive distributed lag model and bounds cointegration tests, and to study the stability of the estimated model. The cumulative sum of recursive residuals test and the cumulative sum of recursive residuals squares tests are used to assess the consistency of the model parameters. © 2021 Natural Sciences Publishing. All rights reserved.