我国国债发行规模中的协整和ECM实证分析

本文应用协整理论和方法,对1979年以来的我国长期宏观经济变量的数据序列进行了单位根检验与协整检验,建立了我国国债发行规模的长期均衡方程和短期误差修正模型(ECM),依据于此模型较好地解释了我国国债发行规模与宏观经济变量之间存在的长期均衡关系以及国债发行规模短期变动的影响因素,同时结果表明该模型也具有很强的预测功能.     

单位与自然人共同犯罪若干问题研究

单位可与自然人构成共同犯罪,此共同犯罪的数额标准应当根据具体情况区别适用。若本单位直接责任人员实施了执行单位意志的职务行为,还以个人意志实施了职责范围外的行为,则可认定该直接责任人员与本单位构成共同犯罪,对直接责任人员数罪并罚。     

关于十年来翻译单位问题研究的述评

回顾了最近十年来中国译学界对翻译单位的研究,分析了句子、段落、语篇这三个主流翻译单位的利弊及近年来的综合道路走向,对未来翻译单位问题的研究提出了一些展望。认为只单纯在理论方面探讨选择何种翻译单位存在一定局限性,未来对翻译单位的探索可考虑集中在实证研究范围内。     

就业市场中“学校歧视”的表征、成因与治理

基于与岗位工作属性无关的先赋性条件对求职者进行选聘导致就业不公平.毕业学校与个体能力不直接相关,因而用人单位在大学毕业生招聘及后续人力资源管理中设置毕业学校门槛的行为属于就业歧视."学校歧视"是隐性的就业歧视,主要表征为用人主体在招聘过程中对不同学校毕业学生区别对待、内部劳动力市场中基于毕业学校差异的同工不同酬."学校歧视"之所以产生,既有我国高等教育发展不平衡的教育原因,也有用人单位控制人力资源成本的经济原因,更有对高等教育功能的认识偏差.治理"学校歧视",应多元主体协同,努力促进我国高等教育均衡协调发展,不断加强劳动力市场的制度规范和标准化建设,促进人才供需双方目标趋同,共同维护公平就业环境.     

牵连犯的犯罪属性和处断原则——以宽严相济的刑事政策为视角

牵连犯因行为人主观意思上的牵连或者客观行为上的牵连而减轻了行为的社会危害性和人身危险性,为了贯彻宽严相济刑事政策之“轻刑化”理念,“牵连关系”的界定应该采用较为宽泛的标准——主观和客观择一说;牵连犯之“轻轻重重”的犯罪属性,与宽严相济的刑事政策基础理念相暗舍;对于牵连犯之处断要“宽严相济”,即牵连犯之定罪要从宽——择一重定罪而不是数罪并罚,牵连犯之处罚要从严——“择一重从重处罚”而非“择一重处罚”;牵连犯“择一重从重处罚”应该统一规定于刑法总则之中,并删除所有刑法分则关于牵连犯之具体处罚规定。     

大学毕业生就业市场规范及发展策略研究

依据市场举办者的不同,大学毕业生就业市场可分为国家各级政府举办、高等学校举办、各类非政府组织的人才中介机构举办三种类型;按照市场存在形式,毕业生就业市场又可分为有形市场和无形市场。以上各类大学毕业生就业市场各有优势与不足。规范及发展大学毕业生就业市场,要求国家各级政府部门、高等学校、人才中介机构有效合作,逐步走向联合,建立和完善新的市场体系。同时还要提高有形市场效率,大力发展无形市场。     

英语专业八级口译评分标准的优化

通过对英语专业八级口语测试中口译评分标准的分析,指出两个有待改进的问题,即只有准确度一个测评项目使得评测不够全面,口译评分单位确定不准确造成评分不够合理。可以在口译评分项目中增加“口译表达”一项,根据语义信息重点调整口译的“评分单位”,来优化准确度的评分标准。     

Estimation When a Parameter is on a Boundary

This paper establishes the asymptotic distribution of an extremum estimator when the true parameter lies on the boundary of the parameter space. The boundary may be linear, curved, and/or kinked. Typically the asymptotic distribution is a function of a multivariate normal distribution in models without stochastic trends and a function of a multivariate Brownian motion in models with stochastic trends. The results apply to a wide variety of estimators and models. Examples treated in the paper are: (i) quasi-ML estimation of a random coefficients regression model with some coefficient variances equal to zero and (ii) LS estimation of an augmented Dickey-Fuller regression with unit root and time trend parameters on the boundary of the parameter space.     

Empirical Limits for Time Series Econometric Models

This paper characterizes empirically achievable limits for time series econometric modeling and forecasting. The approach involves the concept of minimal information loss in time series regression and the paper shows how to derive bounds that delimit the proximity of empirical measures to the true probability measure (the DGP) in models that are of econometric interest. The approach utilizes joint probability measures over the combined space of parameters and observables and the results apply for models with stationary, integrated, and cointegrated data. A theorem due to Rissanen is extended so that it applies directly to probabilities about the relative likelihood (rather than averages), a new way of proving results of the Rissanen type is demonstrated, and the Rissanen theory is extended to nonstationary time series with unit roots, near unit roots, and cointegration of unknown order. The corresponding bound for the minimal information loss in empirical work is shown not to be a constant, in general, but to be proportional to the logarithm of the determinant of the (possibility stochastic) Fisher–information matrix. In fact, the bound that determines proximity to the DGP is generally path dependent, and it depends specifically on the type as well as the number of regressors. For practical purposes, the proximity bound has the asymptotic form (K/2)log n, where K is a new dimensionality factor that depends on the nature of the data as well as the number of parameters in the model. When ‘good’ model selection principles are employed in modeling time series data, we are able to show that our proximity bound quantifies empirical limits even in situations where the models may be incorrectly specified. One of the main implications of the new result is that time trends are more costly than stochastic trends, which are more costly in turn than stationary regressors in achieving proximity to the true density. Thus, in a very real sense and quantifiable manner, the DGP is more elusive when there is nonstationarity in the data. The implications for prediction are explored and a second proximity theorem is given, which provides a bound that measures how close feasible predictors can come to the optimal predictor. Again, the bound has the asymptotic form (K/2)log n, showing that forecasting trends is fundamentally more difficult than forecasting stationary time series, even when the correct form of the model for the trends is known.     

Subsampling Intervals in Autoregressive Models with Linear Time Trend

A new method is proposed for constructing confidence intervals in autoregressive models with linear time trend. Interest focuses on the sum of the autoregressive coefficients because this parameter provides a useful scalar measure of the long‐run persistence properties of an economic time series. Since the type of the limiting distribution of the corresponding OLS estimator, as well as the rate of its convergence, depend in a discontinuous fashion upon whether the true parameter is less than one or equal to one (that is, trend‐stationary case or unit root case), the construction of confidence intervals is notoriously difficult. The crux of our method is to recompute the OLS estimator on smaller blocks of the observed data, according to the general subsampling idea of Politis and Romano (1994a), although some extensions of the standard theory are needed. The method is more general than previous approaches in that it works for arbitrary parameter values, but also because it allows the innovations to be a martingale difference sequence rather than i.i.d. Some simulation studies examine the finite sample performance.