近代上海远东国际汇兑中心的形成

综观历史上重要的国际汇兑中心的发展轨迹,虽各自所处的背景和优势不同,但一般而言,影响一个地区成为国际汇兑中心的最为主要的条件有三:经济规模、金融因素和政治环境。至20世纪二三十年代,上海正是具备了相当数量的汇兑业务量、一定数量的国际汇兑办理机构、品种齐全的汇兑业务种类、符合国际惯例的汇价体系、外汇管制的全面放开等特殊历史条件,与远东唯一的金市场、大条银市场相互套做,形成多角汇兑关系,成为远东汇兑市场之中心,在空间和功能上对伦敦、纽约等欧美国际金融中心起补充作用。上海外汇市场成为远东汇兑市场之中心,次于伦敦、纽约,而成为世界三大汇兑市场之一。     

商业信用风险管理绩效影响因素的实证分析

近年来,Jianmin Jia等学者对"我国信用风险管理现状"及其相关问题进行研究,取得一定的研究成果,但相应研究中存在着"简单使用线性回归,未能消除变量间的相互影响"等局限。基于对已有相关研究成果的评析,并运用因子分析和Pearson相关分析方法,对我国商业信用风险管理绩效的影响因素进行实证研究。研究表明:商业信用风险管理绩效的关键影响因素为"信用管理职能设置"、"客户资信评估"和"信用政策制定",我国企业应从"完善信用风险管理职能"、"改善信息不对称状况"和"提高信用政策制定水平"三方面提高风险管理绩效。     

Marginal likelihood estimation from the Metropolis output: tips and tricks for efficient implementation in generalized linear latent variable models

The marginal likelihood can be notoriously difficult to compute, and particularly so in high-dimensional problems. Chib and Jeliazkov employed the local reversibility of the Metropolis–Hastings algorithm to construct an estimator in models where full conditional densities are not available analytically. The estimator is free of distributional assumptions and is directly linked to the simulation algorithm. However, it generally requires a sequence of reduced Markov chain Monte Carlo runs which makes the method computationally demanding especially in cases when the parameter space is large. In this article, we study the implementation of this estimator on latent variable models which embed independence of the responses to the observables given the latent variables (conditional or local independence). This property is employed in the construction of a multi-block Metropolis-within-Gibbs algorithm that allows to compute the estimator in a single run, regardless of the dimensionality of the parameter space. The counterpart one-block algorithm is also considered here, by pointing out the difference between the two approaches. The paper closes with the illustration of the estimator in simulated and real-life data sets.     

跨国并购与我国企业技术进步的相关性分析

近年来，跨国并购逐渐成为我国企业国际化发展以及构建核心竞争能力的重要途径，而跨国并购能否对我国企业的技术进步起到积极的作用则成为理论研究的热点问题。针对此，采用全要素指标体系，分析了1991~2007年全要素增长率与跨国并购增长率之间的关系，认为:跨国并购的变化对技术进步具有一定的影响关系，但是由于技术并购在对外投资中的比重偏低，过分重视了技术的引进，忽视了技术的吸收消化过程等因素的影响，因此这种影响的效果并不显著。     

Key success factors when implementing a green-manufacturing system

This study proposes a three-layer assessment model to evaluate the performance of a green-manufacturing system (GMS) and identify the key success factors of its real-world implementation. The top layer of the model includes three dimensions – green design, green-manufacturing process and green packaging. The second and third layers include ten strategic subjects and 74 assessment factors, respectively. The weights in each layer are collected via a questionnaire and calculated using the analytical network process (ANP). Based on the ANP, the five key success factors for implementing the GMS in a company are proportion of non-toxic materials, compliance with eco-ordinances, proportion of biodegradable materials, environmental pollution per product and extent of process pollution. The practicality of the proposed model is demonstrated by evaluating three companies that produce similar products and share similar business models. The proposed model provides a valuable reference for real-world implementation of the GMS.     

论语篇连贯的等级性

论述了连贯是语篇的本质属性,语篇没有连贯与否的区分,只有连贯强弱之分,即语篇只存在连贯的等级性问题。并从语篇外部分析了共有知识和语域的选择以及语篇内部衔接手段的运用都是制约语篇连贯等级性的因素。     

吉林省FDI影响因素的实证分析

改革开放以来,随着吉林省投资环境的不断优化,流入吉林省的FDI数量不断增多。使用因子分析与回归分析方法对吉林省FDI流入的影响因素进行实证分析,得出的结论是：影响吉林省FDI流入的综合因素在不断提高。其中,不断扩大的市场规模和较低的要素成本是影响吉林省FDI流入的重要因素,相比之下,较低的对外开放度和不稳定的能源供应阻碍了FDI的流入。     

Bayesian Approaches to Nonparametric Estimation of Densities on the Unit Interval

This paper investigates nonparametric estimation of density on [0, 1]. The kernel estimator of density on [0, 1] has been found to be sensitive to both bandwidth and kernel. This paper proposes a unified Bayesian framework for choosing both the bandwidth and kernel function. In a simulation study, the Bayesian bandwidth estimator performed better than others, and kernel estimators were sensitive to the choice of the kernel and the shapes of the population densities on [0, 1]. The simulation and empirical results demonstrate that the methods proposed in this paper can improve the way the probability densities on [0, 1] are presently estimated.     

Influence in covariance structure analysis: with an application to confirmatory factor analysis

Influence functions are derived for the parameters in covariance structure analysis, where the parameters are estimated by minimizing a discrepancy function between the assumed covariance matrix and the sample covariance matrix. The case of confirmatory factor analysis is studied precisely with a numerical example. Comparing with a general procedure called one-step estimation, the proposed procedure has two advantages:1) computing cost is cheaper, 2) the property that arbitrary influence can be decomposed into a fi-nite number of components discussed by Tanaka and Castano-Tostado(1990) can be used for efficient computing and the characterization of a covariance structure model from the sensitivity perspective. A numerical comparison is made among the confirmatory factor analysis and some procedures of ex-ploratory factor analysis by using the decomposition mentioned above.     

Computational algorithms for the factor model

Algorithms for computing the maximum likelihood estimators and the estimated covariance matrix of the estimators of the factor model are derived. The algorithms are particularly suitable for large matrices and for samples that give zero estimates of some error variances. A method of constructing estimators for reduced models is presented. The algorithms can also be used for the multivariate errors-in-variables model with known error covariance matrix.