Score-Driven Interactions for “Disease X” Using COVID and Non-COVID Mortality

Szabolcs Blazsek, William M. Dos Santos and Andreco S. Edwards
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William M. Dos Santos: Stetson-Hatcher School of Business, Mercer University, Macon, GA 31207, USA
Andreco S. Edwards: College of Liberal Arts and Sciences, Mercer University, Macon, GA 31207, USA

Econometrics, 2024, vol. 12, issue 3, 1-24

Abstract: The COVID-19 (coronavirus disease of 2019) pandemic is over; however, the probability of such a pandemic is about 2% in any year. There are international negotiations among almost 200 countries at the World Health Organization (WHO) concerning a global plan to deal with the next pandemic on the scale of COVID-19, known as “Disease X”. We develop a nonlinear panel quasi-vector autoregressive (PQVAR) model for the multivariate t -distribution with dynamic unobserved effects, which can be used for out-of-sample forecasts of causes of death counts in the United States (US) when a new global pandemic starts. We use panel data from the Centers for Disease Control and Prevention (CDC) for the cross section of all states of the United States (US) from March 2020 to September 2022 regarding all death counts of (i) COVID-19 deaths, (ii) deaths that medically may be related to COVID-19, and (iii) the remaining causes of death. We compare the t -PQVAR model with its special cases, the PVAR moving average (PVARMA), and PVAR. The t -PQVAR model provides robust evidence on dynamic interactions among (i), (ii), and (iii). The t -PQVAR model may be used for out-of-sample forecasting purposes at the outbreak of a future “Disease X” pandemic.

Keywords: score-driven time series models; dynamic conditional score (DCS); generalized autoregressive score (GAS); panel data models; coronavirus disease of 2019 (COVID-19) (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2024
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