Efficiency decomposition in dealers from the perspectives of demand forecasting,sales force,and inventory control: a case study

Efficiency decomposition is being able to know the most effective way to improve a company’s organisational efficiency. This study proposes a network performance evaluation model with non-controllable variables to evaluate dealers with an emphasis on their internal processes, including demand forecasting, sales force and inventory control. The proposed model decomposes the organisational efficiency into a weighted average of internal process efficiencies, where the weights associated with the internal processes are data-driven. This is applied to an empirical case consisting of 27 automobile parts dealers in Taiwan. Based on the analysis results, dealers know the relative contribution of each internal process to the organisational efficiency, and inefficient dealers are assigned targets for enhancing the organisational efficiency as well as the process efficiencies.     

考虑交易成本的投资组合效率估计方法

投资组合绩效评价是学术界研究的热点问题。本文在经典的经济学框架下,基于真实前沿面,给出了投资组合效率的明确定义。由于实际投资环境的影响,投资组合优化模型非常复杂,难以获得真实前沿面的解析解,这给投资组合效率的应用带来了很大的困难。本文基于投资组合理论,在投资组合模型所对应的前沿面为凹函数的情况下,采用基于数据的投资组合DEA评价模型构造前沿面来逼近真实的前沿面,从而估计一般情形下投资组合的效率。在此基础上研究了考虑交易成本的投资组合效率评价问题,并用实例说明了本文方法的合理性与可行性。     

企业研发效率测度与比较——以中国各地区大中型工业企业数据为例

研发效率的高低是影响企业研发决策和经济增长效率的重要因素。已有研究在选择分析方法的时候往往只是根据主观判断,并没有给出明确和客观的理由。而且由于在样本区间、指标选择和数据处理等方面存在较大差异,不同研究之间难以进行直接比较,其核算结果也不能作为判断哪种方法有效的依据。本文在相同的样本区间、指标选择和数据处理前提下,针对中国各地区大中型工业企业1996—2005年的面板数据样本,利用索洛剩余核算、随机前沿分析和数据包络分析等方法对中国企业的研发效率进行评价。通过不同指标和方法的分析比较,可以得到相对更为全面和准确的测度,从而对中国企业的研发行为及效率做出较为客观的评价。     

Variable precision rough set theory and data discretisation: an application to corporate failure prediction

Since the seminal work of Pawlak (International Journal of Information and Computer Science, 11 (1982) 341–356) rough set theory (RST) has evolved into a rule-based decision-making technique. To date, however, relatively little empirical research has been conducted on the efficacy of the rough set approach in the context of business and finance applications. This paper extends previous research by employing a development of RST, namely the variable precision rough sets (VPRS) model, in an experiment to predict between failed and non-failed UK companies. It also utilizes the FUSINTER discretisation method which neglates the influence of an ‘expert’ opinion. The results of the VPRS analysis are compared to those generated by the classical logit and multivariate discriminant analysis, together with more closely related non-parametric decision tree methods. It is concluded that VPRS is a promising addition to existing methods in that it is a practical tool, which generates explicit probabilistic rules from a given information system, with the rules offering the decision maker informative insights into classification problems.     

对组织学习、核心竞争能力、战略柔性与企业竞争绩效的理论剖析与实证研究 --探索中国企业增强动态竞争优势之路

在当前超强竞争时代,组织学习、核心竞争能力和战略柔性,越来越成为企业竞争的焦点问题,成为企业营造持久的动态竞争优势的战略武器.本文在对前人有关研究进行回顾、总结和归纳的基础上,开创性地把企业核心竞争能力划分为技术能力、市场营销能力和整合能力这三个关键构成要素,识别出组织学习的三个关键维度,深入剖析了战略柔性的内涵与本质,并在此基础上,论述了它们之间的互动关系及其对企业竞争绩效的影响.最后,本文以来自中国企业的实际调研数据为基础,运用基于偏最小二乘法的结构方程模型(SEM),对上述问题进行了实证分析,剖析了本项研究对中国企业管理的启示.     

金融高频数据的最优抽样频率研究

首先综述了国内外对最优抽样频率的研究成果,并对各种方法的利弊进行了分析比较;然后结合目前我国股市金融高频数据的特点,提出了一种简便易行的最优抽样频率确定方法,并且基于"已实现"双幂次变差和赋权"已实现"波动估计量进行了研究,分别给出了2个估计量的偏差;最后用深证成指的高频数据进行了实证研究。     

基于GCA-DEA-MSVC方法的高校科研平台评价预测研究

高校科研平台评价与预测分析是促进科研工作健康高效发展的重要载体,但数据指标繁冗、逻辑关系复杂、影响因素众多等大大加剧了科研平台运行评价和预测难度。本文从大数据角度出发探索一种基于GCA-DEA-MSVC方法的高校科研平台评价预测方法。首先利用GCA方法从科研平台运行数据库中挖掘、提取出与评价结果密切相关的关键特征指标并进行分类构建特征指标库,然后利用DEA方法对特征指标库数据进行融合,提升数据质量构建相对效率指标库;最后,将特征指标库与相对效率指标库再次融合,基于改进的MSVC方法构建了高效的科研平台运行状态评价分类预测模型,并利用教育部重点实验室评价数据开展了实验研究,验证了所提方法的有效性。     

中国高龄老人生活自理能力纵向动态研究

利用"中国老年健康长寿跟踪调查"1998年、2000年、2002年的跟踪数据分析中国高龄老人ADL的变动趋势及其影响因素。个体增长模型结果显示,高龄老人ADL状况随年龄增长出现了明显的下降趋势,ADL个体差异十分显著。个体之间的差异解释了ADL总差异的31.13%。经历三次追踪调查的高龄老人ADL均值较高,状况较好,下降速度较慢;基期具有3-4项残障的高龄老人,未来ADL下降最为迅速;基期ADL水平高的高龄老人,ADL下降较慢。从ADL的个体影响因素来看,对于基期ADL水平影响最大的变量是"年龄"、"疾病状况"以及"居住安排"等,而对观测期ADL变动速度影响最大的变量是"年龄"以及"过去是否经常从事体力劳动"等。     

A class of weighted dependence measures for bivariate failure time data

This paper considers a class of summary measures of the dependence between a pair of failure time variables over a finite follow-up region. The class consists of measures that are weighted averages of local dependence measures, and includes the cross-ratio-measure and finite region version of Kendall's τ; recently proposed by the authors. Two new special cases are identified that can avoid the need to estimate the bivariate survivor function and that admit explicit variance estimators. Nonparametric estimators of such dependence measures are proposed and are shown to be consistent and asymptotically normal with variances that can be consistently estimated. Properties of selected estimators are evaluated in a simulation study, and the method is illustrated through an analysis of Australian Twin Study data.     

Confidence intervals for secondary parameters following a sequential test

In sequential studies, formal interim analyses are usually restricted to a consideration of a single null hypothesis concerning a single parameter of interest. Valid frequentist methods of hypothesis testing and of point and interval estimation for the primary parameter have already been devised for use at the end of such a study. However, the completed data set may warrant a more detailed analysis, involving the estimation of parameters corresponding to effects that were not used to determine when to stop, and yet correlated with those that were. This paper describes methods for setting confidence intervals for secondary parameters in a way which provides the correct coverage probability in repeated frequentist realizations of the sequential design used. The method assumes that information accumulates on the primary and secondary parameters at proportional rates. This requirement will be valid in many potential applications, but only in limited situations in survival analysis.