Responses to COVID-19 in Southeast Asia: Diverse Paths and Ongoing Challenges

Due to geographical proximity and trade links with China, Southeast Asian countries were among the first to be exposed to and affected by COVID-19. However, despite shared challenges including protecting population health and economic security, policy responses by national governments have been varied and remain so a year into the pandemic. This article critically reviews Southeast Asian countries' approaches to COVID-19 with reference to individual country experiences and Association of Southeast Asian Nations. We discuss key policy responses: leadership, public risk communications, health system preparedness and resilience, economic support and social protection, aid and global health diplomacy, digital technologies, and the region's multilateral response.     

Combining Bond Rating Forecasts Using Logit

Companies sometimes use statistical analysis to anticipate their bond ratings or a change in the rating. However, different statistical models can yield different ratings forecasts, and there is no clear rule for which model is preferable. We use several forecasting methods to predict bond ratings in the transportation and industrial sectors listed by Moody's bond rating service. A variant of the ordered‐logit regression‐combining method of Kamstra and Kennedy 1998 yields statistically significant, quantitatively meaningful improvements over its competitors, with very little computational cost.     

医保改革与风险机制建立浅议

1996年10月,南平市职工医疗保险制度改革正式启动。几年的改革实践,证实这项改革是事关全局的一件大事,是社会主义市场经济体制的一项基础性建设,是社会保障体系必不可少的重要组成部分,也是推进各项改革继续深入进行的重要举措。 一、职工医疗保障制度改革要实现八个“突破” 当前,职工医疗保障制度改     

A neural network approach to the prediction of going concern status

The assessment of a firm's going concern status is not an easy task. To assist auditors, going concern prediction models based on statistical methods such as multiple discriminant analysis and logit/probit analysis have been explored with some success. This study attempts to look at a different and more recent approach—neural networks. In particular, a neural network model of the feedforward, backpropagation type was constructed to predict a firm's going concern status from six financial ratios, using a data set containing 165 non-going concerns and 165 matched going concerns. On an evenly distributed hold-out sample, the trained network model correctly predicted all 30 test cases. The results suggest that neural networks can be a promising avenue of research and application in the going concern area.