基于奖惩机制的建筑供应链工期协调优化研究

在研究由业主和建筑商组成的两级建筑供应链的工期协调优化问题时,首先建立了基于网络图的具有资源约束的传统工期费用交换问题的优化模型。针对传统工期优化模型的缺陷,建立了具有固定资源约束的考虑资金时间价值的工期优化模型。然后从供应链角度,考虑供应链成员决策的相互影响,建立了具有柔性资源约束的基于工期奖惩机制的工期协调优化模型。给出了模型的求解算法。最后结合算例对比了不同工期优化下的结果。结果显示,传统工期优化模型的优化结果很难在考虑资金时间价值的模型中实现,但通过奖惩激励可以实现甚至更进一步改进工期优化结果,同时实现项目工期控制目标协调优化和供应链的帕累托改善。     

国有企业再融资得到了政策倾斜——基于间隔期模型的实证分析

采用间隔期模型对中国上市公司首次公开发行与上市后的第一次再融资的融资间隔期进行了实证分析.研究表明,国有股比例越高,则首发后融资间隔期越短,而且这一结论在考察了公司的特征属性后是稳健的.更进一步的研究发现,国有股比例高的公司往往更达不到发行主管机构制定的关于再融资的净资产收益率标准.这2点发现表明:在股票发行核准制度框架下,国有企业再融资得到了政策倾斜.     

失业保险给付期限差异下的失业持续时间研究

本文对享受不同失业保险给付期限的失业者的失业持续时间差异进行了实证研究。根据工作搜寻理论,应用存活分析方法建立了含有失业保险给付期限因素的失业持续时间模型,并对青岛市失业者登记数据进行了实证研究。结果表明:享受失业保险者的失业持续时间明显长于不享受失业保险者的失业持续时间,在失业保险给付额度相同的条件下,失业保险给付期限与失业持续时间成同向变动关系。     

基于时间特性的中国股市交易集群性特征的研究

本文以自回归条件久期模型(ACD)为基础,选择标志我国股市交易集群性特征的代理变量,建立了刻画该特征的实证模型,检验了我国上海证券所个股的交易过程中集群性问题.实证结果表明,我国证券市场交易过程中的集群性是由于以私人信息为基础的信息交易所引起的,私人信息的进入导致了证券市场在时间方向表现出更大的波动性.     

Flexible parametric estimation technique for a competing risks model with unobserved heterogeneity: a Monte Carlo study

We analyse a flexible parametric estimation technique for a competing risks (CR) model with unobserved heterogeneity, by extending a local mixed proportional hazard single risk model for continuous duration time to a local mixture CR (LMCR) model for discrete duration time. The state-specific local hazard function for the LMCR model is per definition a valid density function if we have either one or two destination states. We conduct Monte Carlo experiments to compare the estimated parameters of the LMCR model, and to compare the estimated parameters of a CR model based on a Heckman–Singer-type (HS-type) technique, with the data-generating process parameters. The Monte Carlo results show that the LMCR model performs better or at least as good as the HS-type model with respect to the estimated structure parameters in most of the cases, but relatively poorer with respect to the estimated duration-dependence parameters.     

Bayesian inference of asymmetric stochastic conditional duration models

This paper extends stochastic conditional duration (SCD) models for financial transaction data to allow for correlation between error processes and innovations of observed duration process and latent log duration process. Suitable algorithms of Markov Chain Monte Carlo (MCMC) are developed to fit the resulting SCD models under various distributional assumptions about the innovation of the measurement equation. Unlike the estimation methods commonly used to estimate the SCD models in the literature, we work with the original specification of the model, without subjecting the observation equation to a logarithmic transformation. Results of simulation studies suggest that our proposed models and corresponding estimation methodology perform quite well. We also apply an auxiliary particle filter technique to construct one-step-ahead in-sample and out-of-sample duration forecasts of the fitted models. Applications to the IBM transaction data allow comparison of our models and methods to those existing in the literature.     

Survival in the U.S. petroleum refining industry

Of the 324 petroleum refineries operating in the U.S. in 1982, only 149 were still in the hands of their original owners in 2007. Using duration analysis, this paper explores why refineries change ownership or shut down. Plants are more likely to ‘survive’ with their original owners if they are older or larger, but less likely if the owner is a major integrated firm, or the refinery is a more technologically complex one. This latter result differs from existing research on the issue. This paper also presents a split population model to relax the general assumption of the duration model that all refiners will eventually close down; the empirical results show that the split population model converges on a standard hazard model; the log-logistic version fits best. Finally, a multinomial logit model is estimated to analyze the factors that influence the refinery plant's choices of staying open, closing, or changing ownership. Plant size, age and technology usage have positive impacts on the likelihood that a refinery will stay open, or change ownership (rather than close down).     

A bias analysis of Weibull models under heaped data

Retrospectively collected duration data are often reported incorrectly. An important type of such an error is heaping—respondents tend to round-off or round-up the data according to some rule of thumb. For two special cases of the Weibull model we study the behaviour of the ‘naive estimators’, which simply ignore the measurement error due to heaping, and derive closed expressions for the asymptotic bias. These results give a formal justification of empirical evidence and simulation-based findings reported in the literature. Additionally, situations where a remarkable bias has to be expected can be identified, and an exact bias correction can be performed.     

A hazard model of the probability of medical school drop-out in the UK

Summary.  From individual level longitudinal data for two entire cohorts of medical students in UK universities, we use multilevel models to analyse the probability that an individual student will drop out of medical school. We find that academic preparedness—both in terms of previous subjects studied and levels of attainment therein—is the major influence on withdrawal by medical students. Additionally, males and more mature students are more likely to withdraw than females or younger students respectively. We find evidence that the factors influencing the decision to transfer course differ from those affecting the decision to drop out for other reasons.     

对外开放形势下的利率风险管理问题研究

我国加入世贸组织后 ,利率市场化进程将加快 ,利率风险将成为我国商业银行的一种主要风险。文章在介绍西方主要的利率风险衡量技术的基础上 ,分析了对外开放形势下 ,我国商业银行利率风险产生的原因 ,并从商业银行、监管当局、金融市场三个层面提出利率风险管理的有关建议