Seminar – Dr. Taoufik Bouezmarni
Dillon Hall 354, Thursday Oct. 27 4-5pm
In-person
Counts toward seminar attendance for MSc and PhD students in Math & Stats
Title:
Copula-based estimation of health concentration curves with an
application to COVID-19
Abstract:
COVID-19 has created an unprecedented global health crisis that caused
millions of infections and deaths worldwide. Many, however, argue that
pre-existing social inequalities have led to inequalities in infection and
death rates across social classes, with the most-deprived classes are worst
hit. In this paper, we derive semi/non-parametric estimators of Health
Concentration Curve (HC) that can quantify inequalities in COVID-19
infections and deaths and help identify the social classes that are most at
risk of infection and dying from the virus. We express HC in terms of copula
function that we use to build our estimators of HC. For the semi-parametric
estimator, a parametric copula is used to model the dependence between
health and socio-economic variables. The copula function is estimated using
maximum pseudo-likelihood estimator after replacing the cumulative
distribution of health variable by its empirical analogue. For the
non-parametric estimator, we replace the copula function by a Bernstein
copula estimator. Furthermore, we use the above estimators of HC to derive
copula-based estimators of health Gini coefficient. We establish the
consistency and the asymptotic normality of HC's estimators. Using different
data-generating processes and sample sizes, a Monte-Carlo simulation
exercise shows that the semiparametric estimator outperforms the smoothed
nonparametric estimator, and that the latter does better than the empirical
estimator in terms of Integrated Mean Squared Error. Finally, we run an
extensive empirical study to illustrate the importance of HC's estimators
for investigating inequality in COVID-19 infections and deaths in the U.S.
The empirical results show that the inequalities in state's socio-economic
variables like poverty, race/ethnicity, and economic prosperity are behind
the observed inequalities in the U.S.'s COVID-19 infections and deaths.
Co-authors: Mohamed Doukali (University of East Anglia, UK) and Abderrahim Taamouti (University of Liverpool, UK)