Dying together: A convergence analysis of fatalities during COVID-19.

Governments implemented countermeasures to mitigate the spread of the COVID-19 virus. This had a severe effect on the economy. We examine convergence patterns in the evolution of COVID-19 deaths across countries.

Stochastic dominance spanning and augmenting the human development index with institutional quality.

Unlabelled: The well-known Human Development Index (HDI) goes beyond a single measure of well-being as it is constructed as a composite index of achievements in education, income, and health dimensions. However, it is argued that the above dimensions do not reflect the overall well-being, and new indicators should be included in its construction. This paper uses stochastic dominance spanning to test the inclusion of additional institutional quality (governance) dimensions to the HDI, and we examine whether the augmentation of the original set of welfare dimensions by an additional component leads to distributional welfare gains or losses or neither.

The Effect of Pollution on the Spread of COVID-19 in Europe.

Unlabelled: This study investigates empirically how air pollution in earlier periods as measured by three air pollutants, namely , , and may have affected the spread and fatality of COVID-19 in 31 European countries. Using panel data with fixed effects to examine the relationship between previous exposure to air pollution and COVID-19 new cases and COVID-19 deaths, we find that previous air pollution levels have both acted as an important factor in explaining the COVID-19 spread and its high fatality rate. This result may explain the negative impact that these pollutants may have on health and in particular on the respiratory functions that are mainly attacked by the virus.

Quantile forecast combination using stochastic dominance.

This paper derives optimal forecast combinations based on stochastic dominance efficiency (SDE) analysis with differential forecast weights for different quantiles of forecast error distribution. For the optimal forecast combination, SDE will minimize the cumulative density functions of the levels of loss at different quantiles of the forecast error distribution by combining different time-series model-based forecasts. Using two exchange rate series on weekly data for the Japanese yen/US dollar and US dollar/Great Britain pound, we find that the optimal forecast combinations with SDE weights perform better than different forecast selection and combination methods for the majority of the cases at different quantiles of the error distribution.