摘要 : Using a time-varying parameter-structural vector autoregressive (TVP-SVAR) model, this study investigates the dynamic impact of uncertainty caused by worldwide pandemics on industrial productivity growth. We discover that the coro... 展开 Using a time-varying parameter-structural vector autoregressive (TVP-SVAR) model, this study investigates the dynamic impact of uncertainty caused by worldwide pandemics on industrial productivity growth. We discover that the coronavirus has a negative influence on industrial production growth rates across economic blocs (i.e., United States, Developed, and Emerging nations). We also discover that, since 2016, there has been a considerable rise in the comovement of industrial production growth rates. We also employ the dynamic volatility connectedness methodology and find that the industrial productivity growth of Emerging nations economic bloc, and DPUI is observed to be net transmitters of volatility, whereas the industrial productivity growth of United States and other developed nations economic blocs are found to be net recipients of volatility throughout the sample periods. Furthermore, we find that the dynamic total connectedness among the variables under study is observed to be very strong and time-varying. 收起