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Panel Vector Auto-Regressive Model For COVID-19 Infected Cases and Deaths

Author

Listed:
  • Rajarathinam
  • A.
  • Anju
  • J.B
Abstract
This study aims to model the dynamic relationships between the number of COVID-19 infected cases and deaths in all the districts of Kerala state, India, from January 2021 to December 2021 based on the panel vector auto-regressive model. The random effect panel vector auto-regressive model of order two was found suitable to model dynamic relationships. This model explains 62 % variations in the endogenous variable, deaths (number of deaths). The exogenous variable deaths  (-1) are highly significant, whereas the exogenous variable cases (-1) are significant at a 5% level. Both of these exogenous variables positively influence the endogenous variable. The other exogenous variables, viz., deaths (-2) and cases (-2), are non-significant. The Durbin-Watson test statistic value confirms the independence of the residuals, and the Wald test confirms the validity of the significance of the estimated regression coefficients.  JEL classification numbers: E18, HO, I1, J64, J88.

Suggested Citation

  • Rajarathinam & A. & Anju & J.B, 2022. "Panel Vector Auto-Regressive Model For COVID-19 Infected Cases and Deaths," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 11(4), pages 1-2.
    • Handle: RePEc:spt:stecon:v:11:y:2022:i:4:f:11_4_2
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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Fixed and Random Effect Models; Panel VAR model; Cointegration test; Levin-Lin-Chu unit root test; Granger causality test; Hausan test; Wald test.;
    All these keywords.

    JEL classification:

    • I1 - Health, Education, and Welfare - - Health
    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search
    • J88 - Labor and Demographic Economics - - Labor Standards - - - Public Policy

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