出口二元边际、行业异质性与劳动生产率

基于行业异质性视角,文章主要考察出口二元边际对劳动生产率的影响.利用UN Comtrade数据库,采用HK指数法测度中国出口深度边际和出口广度边际,发现中国出口产品种类基本覆盖了世界全部种类的90％.进一步地,借助聚类分析方法综合多种因素区分行业劳动技能异质性,出口二元边际与劳动生产率关系的实证结果显示:总体上出口深度边际显著推动劳动生产率提高,出口广度边际对劳动生产率产生抑制作用.更为重要的是,出口深度边际对劳动生产率的促进作用仅存在于高技能行业,受限于行业产品技术含量和劳动技能水平等,低技能和中等技能行业出口深度边际出现贫困化增长现象.出口种类扩张的生产率损失超过出口溢出效应的生产率收益,出口广度边际对高中低技能行业劳动生产率具有不同程度的负向影响.     

SO42-/TiO2催化合成乙酸乙酯的实验设计

SO4^2-／TiO2固体超强酸薄膜化催化剂能完全代替浓硫酸作催化剂,用乙酸、乙醇来合成乙酸乙酯。在膜催化器上,考察了薄膜化催化剂对乙醇和乙酸合成乙酸乙酯的催化性能。结果表明,薄膜化催化剂显著提高了催化活性;当n（乙醇）：n（乙酸）=1．5：1,原料流量3．0mL／h,在反应温度为110℃时,乙酸乙酯产率可达94．8％,且薄膜催化剂的处理很方便,催化剂再生后可重复使用,不污染环境。     

企业孵化器绩效评价研究综述

国外主要是通过大量调查,对BI运行的效果进行研究。这种研究,取得了大量资料,获得了很多有用信息,便于明确政府的政策效果,明确BI的价值与意义。其不足主要表现在理论研究薄弱和系统研究缺乏。国内有关BI的绩效研究涉及了BI发展的许多问题,范围较宽,得到了许多相关BI绩效评价理论与方法的积累,但由于其延用了西方的理论与方法,因而同样存在经验描述多,理论探索少,缺乏系统的理论支撑及相关的绩效评价方法与指标体系。     

青海话中的词缀"头"

青海话中的词缀“头”除了有与汉语普通话词缀“头”相同的特点外,还能够放在动词词根和形容词词根语素的后面,构成一个新的名词,表示某事是否值得做或必要性的主观评价,也可以指对象或状态;这种方式构成的名词是一个开放的系统,只存在于具体语境之中。文章还进一步探讨了这种用法的起源,并与其它方言中的词缀“头”作了对比分析。     

论海事英语中英文翻译

针对船员汉译英所遇到的关于翻译方法或翻译技巧的困惑,分析汉英双语特征,探讨翻译标准,并提出在文体和文法方面的翻译原则以及使译文“忠实、通顺”的翻译技巧。     

人民币实际汇率波动与中国对美出口

运用协整方法对人民币实际汇率波动与中国对美出口的关系进行实证研究。结果表明：中国对美出口收入效应极强,中国对美出口汇率波动效应较弱,中国对美出口的价格效应不存在。在美国经济可能陷入衰退的情况下,中国政府应暂缓人民币升值和人民币汇率形成机制改革的步伐。     

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.     

Designing and integrating composite networks for monitoring multivariate gaussian pollution fields

Networks of ambient monitoring stations are used to monitor environmental pollution fields such as those for acid rain and air pollution. Such stations provide regular measurements of pollutant concentrations. The networks are established for a variety of purposes at various times so often several stations measuring different subsets of pollutant concentrations can be found in compact geographical regions. The problem of statistically combining these disparate information sources into a single 'network' then arises. Capitalizing on the efficiencies so achieved can then lead to the secondary problem of extending this network. The subject of this paper is a set of 31 air pollution monitoring stations in southern Ontario. Each of these regularly measures a particular subset of ionic sulphate, sulphite, nitrite and ozone. However, this subset varies from station to station. For example only two stations measure all four. Some measure just one. We describe a Bayesian framework for integrating the measurements of these stations to yield a spatial predictive distribution for unmonitored sites and unmeasured concentrations at existing stations. Furthermore we show how this network can be extended by using an entropy maximization criterion. The methods assume that the multivariate response field being measured has a joint Gaussian distribution conditional on its mean and covariance function. A conjugate prior is used for these parameters, some of its hyperparameters being fitted empirically.     

Correlation in a Bayesian framework

The authors consider the correlation between two arbitrary functions of the data and a parameter when the parameter is regarded as a random variable with given prior distribution. They show how to compute such a correlation and use closed form expressions to assess the dependence between parameters and various classical or robust estimators thereof, as well as between p‐values and posterior probabilities of the null hypothesis in the one‐sided testing problem. Other applications involve the Dirichlet process and stationary Gaussian processes. Using this approach, the authors also derive a general nonparametric upper bound on Bayes risks.     

Differential equation models for statistical functions

Differential equations have been used in statistics to define functions such as probability densities. But the idea of using differential equation formulations of stochastic models has a much wider scope. The author gives several examples, including simultaneous estimation of a regression model and residual density, monotone smoothing, specification of a link function, differential equation models of data, and smoothing over complicated multidimensional domains. This paper aims to stimulate interest in this approach to functional estimation problems, rather than provide carefully worked out methods.