Modeling of COVID-19 Panel Time-series Data Based on Generalized Method of Moments | Chapter 6 | An Overview on Business, Management and Economics Research Vol. 1

The main objective of the episode is to inspect the dynamic friendships between the number of COVID-19 infected cases and demise in all the regions of Karnataka state, India, during July 2020 to December 2021 established the Arellano-Bond estimator using the statement method of importance (GMM). The panel GMM model with the first dissimilarity transformation was establish suitable for learning the dynamics of the number of fatality due to COVID-19 contaminations. For analyzing the dynamics of the number of fatality caused by COVID-19 contaminations over time, the committee GMM model with the first dissimilarity transformation was proved to be useful. The individual-period delay (DEATHS(-1)) has a positive and meaningful effect on the number of deaths (DEATH). The committee GMM method accompanying the first difference transformation has existed calculated and bestowed in Table 15. R2 is not used as a mathematical standard for determining the model's excellence of fit, but the J-statistics determine the validity of the instrument changing used in the model. The Wald test strengthens the model's descriptive ability and certifies the relevance of the coefficients. The number of fatalities earlier t is positively guide the number of fatalities during the preceding period. Additionally, the number of infected cases has a helpful and considerable enduring impact on the end of life rate. Granger pairwise causality test tells the existence of bi-directional causality friendships between the COVID-19 polluted cases and deaths.

Author(s) Details:

A. Rajarathinam,
Manonmaniam Sundaranar University, Tamil Nadu, India.

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