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Gaetano Perone

doi:10.24171/j.phrp.2023.0072L

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Correspondence Comments on the article "Time-series comparison of COVID-19 case fatality rates across 21 countries with adjustment for multiple covariates": Gaetano Perone; Osong Public Health and Research Perspectives 2023;14(2):146-146.
DOI: https://doi.org/10.24171/j.phrp.2023.0072L
Published online: March 24, 2023

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Department of Economics and Management, University of Pisa, Pisa, Italy

Gaetano Perone Department of Economics and Management, University of Pisa, Pisa, Italy E-mail: gaetano.perone@ec.unipi.it

• Received: March 14, 2023 • Accepted: March 20, 2023

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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To the Editor:

I read the recently published article by Kim et al. [1]. On page 424 [1], the authors state, referring to my paper [2], that “other research using time-series cross-sectional data appears to have underestimated the impact of autocorrelation and heteroscedasticity”. However, this statement is incorrect and unfounded for 2 reasons. First, I used cross-sectional data rather than panel data, so there was no time component. The corollary is that residuals cannot be serially correlated. It makes no sense to consider autocorrelation in this case. Second, as shown in Section 5.1 of Perone [2], I safely considered heteroscedasticity in my paper: “Furthermore, since Breusch and Pagan (1979) and Shapiro and Wilk (1965) tests allowed to accept the null hypothesis of homoscedasticity and normality of residuals, models seemed well specified. However, due to the small sample, I preferred to adopt a conservative approach, by applying the HC2 correction proposed by MacKinnon and White (1985)” [3−5]. As a result, autocorrelation and heteroscedasticity issues have no bearing on the results of my paper.

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The author has no conflicts of interest to declare.

References

1. Kim Y, Kim BI, Tak S. Time-series comparison of COVID-19 case fatality rates across 21 countries with adjustment for multiple covariates. Osong Public Health Res Perspect 2022;13:424−34.Article PubMed PMC PDF
2. Perone G. The determinants of COVID-19 case fatality rate (CFR) in the Italian regions and provinces: an analysis of environmental, demographic, and healthcare factors. Sci Total Environ 2021;755(Pt 1):142523. Article PubMed PMC
3. Breusch TS, Pagan AR. A simple test for heteroscedasticity and random coefficient variation. Econometrica 1979;47:1287−94.Article
4. Shapiro SS, Wilk MB. An analysis of variance test for normality (complete samples). Biometrika 1965;52:591−611.Article
5. MacKinnon JG, White H. Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties. J Econom 1985;29:305−25.Article

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Time-series comparison of COVID-19 case fatality rates across 21 countries with adjustment for multiple covariates