内外部网络口碑与在线销售的动态交互过程——基于分层贝叶斯模型的解析

与以往单纯以内部网络口碑、相互独立的内部和外部网络口碑为研究对象,分析网络口碑与在线销售的关系不同。基于评论环境理论,采用分层贝叶斯模型,以当当网、京东、亚马逊和豆瓣网为数据搜集对象,在充分考虑网络口碑异质性及其来源多元化的条件下,结合外部网络口碑平台信息提供的特点,在进一步引入免费试样因素的基础上,对内外部网络口碑与在线销售的动态交互作用进行研究。结果发现：在异质性网络平台并存且效价差异较小的情境下,效价不再是影响在线销售的关键,而内部网络口碑数量、数量信息熵才是影响在线销售的关键,反过来在线销售也刺激了网络口碑数量的提升。虽然外部网络口碑的存在削弱了这一环形机制的积极影响,但免费试样可有效削弱该负向调节机制。     

新凯恩斯主义视域下中国近十年货币政策轨迹

基于开放经济新凯恩斯主义货币政策动态随机一般均衡模型的新凯恩斯主义菲利普斯曲线、动态IS曲线、新凯恩斯主义工资菲利普斯曲线,考察了不同货币政策机制对中国近十年货币政策轨迹的拟合度。通过全样本预测,发现混合规则对于中国近十年的产出缺口具有最佳的拟合度,单一的价格规则或数量规则均无法较好地拟合中国现实数据;二阶矩匹配的结果进一步证实了这一论断。     

中国高耗能行业能源消费的贝叶斯非对称影响效应研究

构建双机制的非线性FAVAR模型,从贝叶斯角度出发,研究中国高耗能行业能源消费对宏观经济以及环境污染因素的影响效应。利用中国高耗能行业的能源消费以及废气排放数据,结合贝叶斯方法进行非线性FAVAR模型的参数估计,结果发现中国高耗能行业的能源消费增加和减少对国内生产总值、外商直接投资等宏观经济因素以及主要环境污染因素都具有非对称影响效应。但是,高耗能能源消费增加或减少的冲击一般都是短期效应,不具有长期影响效应。     

提前偿贷风险下催生次贷危机的微观主体行为分析——基于不完全信息动态博弈的视角

始于2007年的次贷危机所诱发的金融海啸席卷全球,当后危机时代逐渐走近之时,各种关于这场危机原因的探讨从宏观层面上作出了种种解释。但微观主体的理性决策行为与群体的不可预知性促使笔者从各参与主体的利益状况出发,采用博弈论的原理对次贷危机深层次的原因进行进一步的剖析。在不完全信息的假定下,本文从静态和动态两方面分别考察了贷款人、商业银行、投资者以及特定目的机构的收益函数,正是由于商业银行在贝叶斯均衡与序列均衡中都存在获利的可能,从而催生了整个证券化过程的展开。     

Alternative Methods for Computing the Sensitivity of Complex Surveillance Systems

Stochastic scenario trees are a new and popular method by which surveillance systems can be analyzed to demonstrate freedom from pests and disease. For multiple component systems—such as a combination of a serological survey and systematically collected observations—it can be difficult to represent the complete system in a tree because many branches are required to represent complex conditional relationships. Here we show that many of the branches of some scenario trees have identical outcomes and are therefore redundant. We demonstrate how to prune branches and derive compact representations of scenario trees using matrix algebra and Bayesian belief networks. The Bayesian network representation is particularly useful for calculation and exposition. It therefore provides a firm basis for arguing disease freedom in international forums.     

存在道德风险的委托--代理模型的激励和监督问题

首先简要地介绍存在委托代理关系问题的激励理论,然后对存在道德风险的委托-代理模型进行分析并认为,委托人对代理人努力程度的监督与对自然状态的观测所产生的效果具有可替代性,最后通过对模型的拓展和信息非对称问题的贝叶斯分析得出了一些有新意的结论.     

Evaluating Probabilistic Forecasts with Bayesian Signal Detection Models

We propose the use of signal detection theory (SDT) to evaluate the performance of both probabilistic forecasting systems and individual forecasters. The main advantage of SDT is that it provides a principled way to distinguish the response from system diagnosticity, which is defined as the ability to distinguish events that occur from those that do not. There are two challenges in applying SDT to probabilistic forecasts. First, the SDT model must handle judged probabilities rather than the conventional binary decisions. Second, the model must be able to operate in the presence of sparse data generated within the context of human forecasting systems. Our approach is to specify a model of how individual forecasts are generated from underlying representations and use Bayesian inference to estimate the underlying latent parameters. Given our estimate of the underlying representations, features of the classic SDT model, such as the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC), follow immediately. We show how our approach allows ROC curves and AUCs to be applied to individuals within a group of forecasters, estimated as a function of time, and extended to measure differences in forecastability across different domains. Among the advantages of this method is that it depends only on the ordinal properties of the probabilistic forecasts. We conclude with a brief discussion of how this approach might facilitate decision making.     

Efficiency determinants in retail stores: a Bayesian framework

This paper analyzes the impact of vertical integration, age, geographic expansion, and low price strategy on the cost efficiency of retail stores. We test our hypotheses using the innovative Bayesian frontier methodology. The data involve a sample of Spanish retail stores that operate in a highly competitive and dynamic environment. From the results, it is clear that cost efficiency is higher for stores that have: longer years in business, stronger geographical presence, and lower price offerings. Vertical integration, on the other hand, is negatively related to efficiency. Further discussions of these findings and related managerial implications are provided in the paper.     

Global Robustness with Respect to the Loss Function and the Prior

We propose a class [I,S] of loss functions for modeling the imprecise preferences of the decision maker in Bayesian Decision Theory. This class is built upon two extreme loss functions I and S which reflect the limited information about the loss function. We give an approximation of the set of Bayes actions for every loss function in [I,S] and every prior in a mixture class; if the decision space is a subset of , we obtain the exact set.     

Decision Making Under Risk: A Comparison of Bayesian and Fuzzy Set Methods

A classical decision problem is considered where a decision maker is to choose one of a number of actions each offering different consequences. The outcome from a choice of action is uncertain because it depends on the existing state of Nature. Also, the outcome, once an action and state of Nature are specified, may be a vector or a random vector. The decision maker employs both Bayesian methods and fuzzy set techniques to handle the uncertainties. The decision maker is also allowed to use multiple, possibly conflicting, goals in order to determine his best strategy. The Bayesian method produces a set of undominated strategies to choose from, whereas the fuzzy set technique usually produces a unique optimal strategy.