Development of an Autoregressive Distributed Lag Model of COVID-19 Infected Cases and Deaths

Abstract

The majority of research used different regression and time-series models to analyse COVID-19 cases because these models are widely used to analyse the progression or development of diseases. The main goals of the current study are to examine the short- and long-term cointegration relationships between the cumulative number of new COVID-19 infections (X) and the cumulative number of COVID-19-related deaths (Y), to examine the long-run equilibrium relationship between these variables using an autoregressive distributed lag model and bounds cointegration tests, and to examine the stability of the estimated model.  In order to evaluate the consistency of the model parameters, two tests-the cumulative sum of recursive residuals test and the cumulative sum of recursive residuals squares test-are utilised.