Health and political regimes: Evidence from quantile regression

This study investigates how political regimes affect health conditions such as infant and child mortality rates and life expectancy using data from 180 countries observed between 1960 and 2013. Panel quantile regression is used to examine the effects at different intervals throughout the distribution of health outcomes. The estimation results indicate that democracy has significant positive effects on health outcomes and that its impacts are greater when health outcomes are worse. These results are robust to different democracy and health indices. The effects of different types of democracies and dictatorships are also considered, that is parliamentary, mixed (semi-presidential) and presidential democracies, and civilian, military and royal dictatorships. The parliamentary form of democracy has the largest positive impact on health outcomes at the worst quantile of health outcomes, although the difference in the impacts of the three types of democracies is not necessarily large. Furthermore, all types of dictatorships have a negative impact on health outcomes, with military dictatorship having the worst outcome when health outcomes are worse. Finally, the effects of democratization on health outcomes are significantly positive when the health outcomes are worse.     

Killing off cohorts: Forecasting mortality of non-extinct cohorts with the penalized composite link model

Mortality forecasting has crucial implications for insurance and pension policies. A large amount of literature has proposed models to forecast mortality using cross-sectional (period) data instead of longitudinal (cohort) data. As a consequence, decisions are generally based on period life tables and summary measures such as period life expectancy, which reflect hypothetical mortality rather than the mortality actually experienced by a cohort. This study introduces a novel method to forecast cohort mortality and the cohort life expectancy of non-extinct cohorts. The intent is to complete the mortality profile of cohorts born up to 1960. The proposed method is based on the penalized composite link model for ungrouping data. The performance of the method is investigated using cohort mortality data retrieved from the Human Mortality Database for England & Wales, Sweden, and Switzerland for male and female populations.     

基于投影模型的不确定语言型多属性决策方法及应用

研究了不确定语言型多属性决策评价结果与决策者对方案的偏好信息之间存在偏差的问题,提出了基于投影模型的不确定多属性决策方法。该方法通过建立与区间型语言标度对应的术语指标矩阵,及方案综合属性值与决策者主观偏好值之间的投影模型,确定属性的权重,然后运用加权法得到方案的综合属性值,利用已有的可能度矩阵排序公式得到决策方案的排序。最后,通过算例对该方法的实用性和有效性进行了说明。     

The Effect of Risk Characteristics on the Willingness to Pay for Mortality Risk Reductions from Electric Power Generation

The objective of this study is to estimate willingness to pay (WTP) for the reduction of mortality risks caused by fossil fuel (natural gas, coal and oil) versus nuclear electric power generation systems and to examine the influence of risk characteristics involved with electric power generation on WTP. A choice experiment was conducted to achieve these objectives. The attributes for nuclear risks in the experiment included the probability of disasters and the expected losses if a disaster occurs. We find evidence of (i) a baseline effect (where WTP is sensitive to hypothetical versus actual baseline expected mortality); (ii) a ‘labeling effect,’ where, surprisingly, the term ‘nuclear’ has no effect on WTP, but the term ‘fossil-fueled power generation’ results in lower WTP; and (iii) disaster aversion, meaning that people focus on the conditional loss from a nuclear disaster, not the probability. We also find that the WTP for reducing deaths from a nuclear disaster is about 60 times the WTP for routine reducing fossil-fuel generation-related deaths.     

死亡率免疫理论及其在长寿风险对冲中的应用

基于死亡率免疫理论探讨保险公司长寿风险的自然对冲问题,研究年龄、性别与保险期限等因素与保单久期和凸性之间的关系,发现保险公司欲达到长寿风险的完全对冲,其寿险业务和年金业务必须达到的最小ρ比例应该等于年金和寿险保单的久期之比,表明保险公司可以通过选择调整寿险和年金业务的比例来对冲长寿风险的影响。     

基于区间数向量投影的主要防御方向选择

一种基于区间数向量投影的主要防御方向选择方法。针对评价指标属性和权重的区间数,构造正理想方案,计算各方案在正理想方案的投影值,根据投影值完成方案的排序优选。实例分析验证了其有效性和适用性。     

Varying Coefficient Regression Models: A Review and New Developments

Varying coefficient regression models are known to be very useful tools for analysing the relation between a response and a group of covariates. Their structure and interpretability are similar to those for the traditional linear regression model, but they are more flexible because of the infinite dimensionality of the corresponding parameter spaces. The aims of this paper are to give an overview on the existing methodological and theoretical developments for varying coefficient models and to discuss their extensions with some new developments. The new developments enable us to use different amount of smoothing for estimating different component functions in the models. They are for a flexible form of varying coefficient models that requires smoothing across different covariates' spaces and are based on the smooth backfitting technique that is admitted as a powerful technique for fitting structural regression models and is also known to free us from the curse of dimensionality.     

Dory & Nemo Early Learning Center

The case requires students to evaluate the current situation and growth options of a small business, Dory & Nemo Early Learning Center (D&N), which provides intergenerational programming to preschool children at a retirement home. The learning center was opened in September 2014, and it was able to make a small profit each year. However, it is projected that there would be a 75 percent decrease in net income from $8,072 in Fiscal 2016 to $1,819 in Fiscal 2017. Davis and Nathans, business partners of D&N, met to discuss the following growth options: (i) Mosaic Retirement Residences' proposal to set up two learning centers at their retirement homes each year for a total of six in three years and (ii) Harmony Retirement Residences' proposal for leasing space to set up learning centers at their retirement homes, one new learning center a year for a total of three in three years. Furthermore, Davis and Nathans had decided to increase the program fee from $320 to $350 per week for the 2017–18 academic session in September. They would also like to reduce their workload from 50 to 40 hours per week, increase their vacation time from two to three weeks, and increase their salaries and bonuses. Students must consider personal objectives of business partners and mission of D&N in the analysis. They learn to identify relevant information for decision making, apply appropriate analytical tools for quantitative analysis, integrate qualitative and quantitative factors in decision making, and make recommendations consistent with analysis.