A hybrid information approach to predict corporate credit risk

This study proposes a hybrid information approach to predict corporate credit risk. In contrast to the previous literature that debates which credit risk model is the best, we pool information from a diverse set of structural and reduced‐form models to produce a model combination based on credit risk prediction. Compared with each single model, the pooled strategies yield consistently lower average risk prediction errors over time. We also find that while the reduced‐form models contribute more in the pooled strategies for speculative‐grade names and longer maturities, the structural models have higher weights for shorter maturities and investment grade names.     

Inferential Causal Models: Integrating Quality & Quantity

In-depth data analysis plus statistical modeling can produce inferentialcausal models. Their creation thus combines aspects of analysis by close inspection,that is, reason analysis and cross-tabular analysis, with statistical analysis procedures,especially those that are special cases of the generalized linear model (McCullaghand Nelder, 1989; Agresti, 1996; Lindsey, 1997). This paper explores some of the roots of this combined method and suggests some new directions. An exercise clarifies some limitations of classic reason analysis by showing how the cross tabulation of variables with controls for test factors may produce better inferences. Then, given the cross tabulation of several variables, by explicating Coleman effect parameters, logistic regressions, and Poisson log-linear models, it shows how generalized linear models provide appropriate measures of effects and tests of statistical significance. Finally, to address a weakness of reason analysis, a case-control design is proposed and an example is developed.     

The Multilevel Approach to Repeated Measures for Complete and Incomplete Data

Repeated measurements often are analyzed by multivariate analysis of variance (MANOVA). An alternative approach is provided by multilevel analysis, also called the hierarchical linear model (HLM), which makes use of random coefficient models. This paper is a tutorial which indicates that the HLM can be specified in many different ways, corresponding to different sets of assumptions about the covariance matrix of the repeated measurements. The possible assumptions range from the very restrictive compound symmetry model to the unrestricted multivariate model. Thus, the HLM can be used to steer a useful middle road between the two traditional methods for analyzing repeated measurements. Another important advantage of the multilevel approach to analyzing repeated measures is the fact that it can be easily used also if the data are incomplete. Thus it provides a way to achieve a fully multivariate analysis of repeated measures with incomplete data. This revised version was published online in June 2006 with corrections to the Cover Date.     

基于AHP＋熵组合的相对发达地区新乡镇建设评价——以苏南三镇为例

面对新农村建设活跃和城市化进程加速，新乡镇建设也初见端倪，选取苏南三镇作为实证地点，构建相对发达地区新乡镇建设评价体系，利用主观赋权的层次分析法（AHP）和客观赋权的熵权法进行组合评价。     

The Random Walk Hypothesis in the Emerging Indian Stock Market

This paper examines the random walk hypothesis in the emerging Indian stock market using daily data on individual stocks. The statistical evidence in this paper rejects the random walk hypothesis. The results suggest that daily returns earned by individual stocks and by an equally weighted portfolio show significant non–linear dependence and persistent volatility effects. The non–linear dependence takes the form of ARCH–type conditional heteroskedasticity and does not appear to be caused by nonstationarity of underlying economic variables. Though conditional volatility is time varying, it does not explain expected returns.     

On the Use of Value Efficiency Analysis and Some Further Developments

The purpose of this paper is to discuss the use of Value Efficiency Analysis (VEA) in efficiency evaluation when preference information is taken into account. Value efficiency analysis is an approach, which applies the ideas developed for Multiple Objective Linear Programming (MOLP) to Data Envelopment Analysis (DEA). Preference information is given through the desirable structure of input- and output-values. The same values can be used for all units under evaluation or the values can be specific for each unit. A decision-maker can specify the input- and output-values subjectively without any support or (s)he can use a multiple criteria support system to assist him/her to find those values on the efficient frontier. The underlying assumption is that the most preferred values maximize the decision-maker's implicitly known value function in a production possibility set or a subset. The purpose of value efficiency analysis is to estimate a need to increase outputs and/or decrease inputs for reaching the indifference contour of the value function at the optimum. In this paper, we briefly review the main ideas in value efficiency analysis and discuss practical aspects related to the use of value efficiency analysis. We also consider some extensions.     

清单计价背景下投标报价策略研究

在建筑市场推行无底招标和清单计价规范的背景下，探讨由业主给定工程量，投标单位填报单价，最后经过验方计价，以单价结算的合同下，投标单位如何根据业主给定工程量可能的变动趋势，基于运筹学的线性规划理论，进行不平衡报价，最大化其收益。     

运用GAHP法建立研发人员绩效考评体系

本文运用群体层次分析法(GAHP)结合研发人员自身的特点,建立多级研发人员绩效考评体系,运用Hadamard凸组合集结判断矩阵,解决了考评体系制定过程中受决策者主观偏好影响较大,考评指标难以定量描述的问题。并结合该体系在一家企业的应用,说明了其在研发人员绩效考评方面应用的有效性。     

基于应变数线性变换的约束广义G-M估计

考虑线性变换对约束线性模型回归系数的影响问题，证明了观测数据的线性变换对于约束模型的影响可以通过一个可用两步约束最小二乘法解决的约束回归问题进行了分析，得到了回归系数的约束可估函数的约束最佳线尾无偏估计不受变换影响的充要条件。     

莆田市发展“医养一体化”服务模式的对策研究

随着银色浪潮来袭,如何养老已成为我国各城市重点关注的问题。将医疗机构、养老机构、社区医疗服务中心当中的医疗资源与养老资源进行共享交流,搭建“医养一体化”平台,是我国养老服务的发展趋势。论文研究了莆田市如何突破传统养老方式的局限性,推行“医养一体化”服务模式,探讨“医养一体化”平台的搭建对策。