证券投资风险预测的E-Bayes估计

本文给出了E-Bayes方法,以上海证券个股五粮液52个连续交易日的收盘价格为例,建立数学模型进行分析和预测,预测结果与市场实际值相当吻合。与灰色系统理论中的GM（1,1）预测模型相比,本文提出的方法预测的精度更高,计算量小。不仅适用于经济系统的分析与预测,也适用于其它系统的分析与预测。     

基于动态贝叶斯网络的邻近下穿隧道深基坑施工风险分析

为了减小邻近既有下穿隧道深基坑施工风险及灾害损失,科学预防施工安全事故,提出了一种基于动态贝叶斯网络（DBN）的深基坑施工风险分析模型。首先,运用BWM（best worst method）确定准则的权重;其次,基于关联规则挖掘风险因素间的相互关系,并以此构建DBN结构模型;最后,以新建厦门北站地下一层社会连廊深基坑工程为例,对提出的方法进行有效性和适用性检验。结果表明：基坑围护结构的安全度在静态被评为“较高”和“极高”的概率分别为34.6%和36.1%,且此结果随着输入风险证据发生动态变化,运用反向推理也能迅速找出围护桩渗水风险;提出的模型能明确邻近既有下穿隧道深基坑施工风险传递过程中的关键风险点,并能进行动态风险预测以及事故后致因诊断,从而实现邻近既有下穿隧道深基坑施工风险的动态管控。所提出的优化DBN模型对工前风险评估、先验分析和风险诊断有较好的适用性和较高的准确性,可为邻近既有隧道深基坑施工过程中的安全管控提供有效的决策支持,大幅提高风险控制效率。     

企业培训策略选择的博弈分析

企业对员工进行培训,可以提高员工的生产率,从而为企业带来更丰厚的利润,然而却又面临着员工跳槽的风险.企业为了挽留经过培训的员工,给予其较高的薪资待遇,结果给企业带来巨大的成本,甚至仍然留不住关键人才.笔者运用信号博弈的方法分析企业在员工培训方面的决策问题,得出多种均衡结果.     

货币政策与风险偏好关系研究——基于动态随机一般均衡模型

为了研究中国货币政策与家庭风险偏好之间的关系,在DSGE框架下运用方差分解和脉冲响应等方法分析不同风险偏好条件下利率冲击和货币供给冲击等对中国经济波动的影响;研究结果表明:首先,货币政策方面的冲击解释了中国经济的大部分波动;其次,代表性家庭风险偏好越小,经济对货币供给变动的反应越灵敏。     

Policy Determinants of School Outcomes under Model Uncertainty: Evidence from South Africa

In this paper, we assess the determinants of secondary school outcomes in South Africa. We use Bayesian model averaging techniques to account for uncertainty in the set of underlying factors that are chosen among a very large pool of explanatory variables in order to identify the subset of explanatory variables most strongly associated with the dependent variable. Our analysis indicates that the socio‐economic background of pupils, demographic characteristics such as population groups (Black and White), as well as geographical locations account for a significant variation in pupils’ achievement levels. We also find that the most robust policy determinants of pupils’ test scores are the availability of a library at school, the use of IT in the classroom as well as school climate.     

机制设计中的履约理论研究综述

显示原理和履约理论是机制设计理论中的两大基石。履约理论把社会选择规则和博弈论联系起来，确保计划者预设的社会目标可以实现。马斯金首先提出了纳什履约理论的充分必要条件，即马斯金单调性和无否决权，随后其他学者提出了其它均衡策略的履约条件。文章论述了履约理论的产生背景和过程，分析了完全信息和不完全信息两种情形下不同的均衡策略履约条件及其精炼，并讨论了履约理论的局限性和未来的研究方向。     

Unveiling covariate inclusion structures in economic growth regressions using latent class analysis

We propose the use of Latent Class Analysis methods to analyze the covariate inclusion patterns across specifications resulting from Bayesian model averaging exercises. Using Dirichlet Process clustering, we are able to identify and describe dependency structures among variables in terms of inclusion in the specifications that compose the model space. We apply the method to two datasets of potential determinants of economic growth. Clustering the posterior covariate inclusion structure of the model space formed by linear regression models reveals interesting patterns of complementarity and substitutability across economic growth determinants.     

Professional forecasters and real-time forecasting with a DSGE model

This paper analyses the real-time forecasting performance of the New Keynesian DSGE model of Galí, Smets and Wouters (2012), estimated on euro area data. It investigates the extent to which the inclusion of forecasts of inflation, GDP growth and unemployment by professional forecasters improve the forecasting performance. We consider two approaches for conditioning on such information. Under the “noise” approach, the mean professional forecasts are assumed to be noisy indicators of the rational expectations forecasts implied by the DSGE model. Under the “news” approach, it is assumed that the forecasts reveal the presence of expected future structural shocks in line with those estimated in the past. The forecasts of the DSGE model are compared with those from a Bayesian VAR model, an AR(1) model, a sample mean and a random walk.     

Spatial modelling of claim frequency and claim size in non-life insurance

In this paper, models for claim frequency and average claim size in non-life insurance are considered. Both covariates and spatial random effects are included allowing the modelling of a spatial dependency pattern. We assume a Poisson model for the number of claims, while claim size is modelled using a Gamma distribution. However, in contrast to the usual compound Poisson model, we allow for dependencies between claim size and claim frequency. A fully Bayesian approach is followed, parameters are estimated using Markov Chain Monte Carlo (MCMC). The issue of model comparison is thoroughly addressed. Besides the deviance information criterion and the predictive model choice criterion, we suggest the use of proper scoring rules based on the posterior predictive distribution for comparing models. We give an application to a comprehensive data set from a German car insurance company. The inclusion of spatial effects significantly improves the models for both claim frequency and claim size, and also leads to more accurate predictions of the total claim sizes. Further, we detect significant dependencies between the number of claims and claim size. Both spatial and number of claims effects are interpreted and quantified from an actuarial point of view.     

Leaving EMU: a real options perspective

The real option implicit in a country's decision of whether to leave an existing monetary union when there is uncertainty over the future benefits of this move is examined. The theoretical model used is calibrated for the current Euro-12 area by proxying policymakers’ inflation preferences with unemployment rates and debt-to-GDP ratios. A robust group of countries is observed that would choose to remain within EMU consisting of Belgium, Finland, Greece and Italy; France and Spain loosely also belong to this core. Only Luxembourg would robustly want to leave EMU; Ireland and The Netherlands, however, complement that core closely.