人民币实际汇率变动对我国贸易收支的影响——主要市场双边贸易收支的实证研究

近年来,我国出现了一方面人民币兑美元和日元实际汇率均升值,而中美贸易呈现顺差持续扩大的趋势,中日贸易则出现逆差持续扩大的趋势;另一方面人民币兑美元实际汇率升值,兑欧元实际汇率贬值,但中美与中欧双边贸易均呈现贸易顺差持续扩大的趋势.这与传统的"J"曲线效应理论相矛盾,形成了所谓的"中国贸易收支之谜".本文采用非平稳时间序列的协整分析方法探讨了人民币实际汇率和贸易收支之间的长期均衡关系,认为"中国贸易收支之谜"的原因在于:中国商品的国外实际汇率弹性与外国商品的国内实际汇率弹性具有显著差异,同时中国商品的外国实际收入弹性与外国商品的实际收入弹性也具有显著差异.导致这一情况出现的深层原因是中国与三个主要贸易伙伴的经济发展水平存在显著差距;此外,欧美等国对华高科技产品出口限制政策也是其对华贸易逆差增长的原因之一.     

关于高校就业率的思考

高校就业率政策在激发高校提高办学质量、重视就业指导工作等方面起着积极的作用。随着我国教育体制改革的深入,高等教育由精英化向大众化转变,大学生就业问题逐渐成为社会关注焦点之一,高校毕业生就业率也日益受到重视。针对目前就业率评价与统计过程中出现的问题,提出理性地看待高校就业率就要合理设置统计时间、科学界定就业率统计途径、结合就业率与就业质量来评价高校就业情况。     

珠三角地区企业员工满意度及离职意愿的探讨

员工的自愿离职直接影响到企业人才的稳定性,也会增加企业的人力资源成本。员工的实际离职产生于离职意愿,而离职意愿与员工满意度密切相关。了解员工对工作的态度,将有助于我们制定相应的人力资源管理的措施,更好地留住企业所需的人才,增强企业的竞争力。文章采取问卷调查的方法,通过方便随机抽样获得1469份有效问卷,被试者来自珠三角地区不同企业的员工。研究内容主要包括:广东珠三角地区的企业员工工作满意度和构成因素,员工离职意愿,人口特征对满意度、离职意愿、工作积极性的影响。     

基于人民币升值的船舶出口业影响研究

在各种主客观因素的作用下,人民币汇率制度改革应运而生,这一汇率变化必将对中国的社会和经济带来巨大的影响。近年来,我国的船舶出口业发展喜人,在国际市场上拥有很强的竞争力,并有着继续壮大的趋势,但随着人民币升值的影响以及船舶出口业的特点,决定了船舶出口业必定要受到较大的冲击和影响。所以在整个贸易大环境下,船舶出口业只有清楚地认识到人民币升值带来的影响,才能正确把握船舶出口的前进方向,运用有效的措施来巩固并促进船舶出口业的发展。     

A permutation-based Bayesian approach for inverse covariance estimation

Abstract

Covariance estimation and selection for multivariate datasets in a high-dimensional regime is a fundamental problem in modern statistics. Gaussian graphical models are a popular class of models used for this purpose. Current Bayesian methods for inverse covariance matrix estimation under Gaussian graphical models require the underlying graph and hence the ordering of variables to be known. However, in practice, such information on the true underlying model is often unavailable. We therefore propose a novel permutation-based Bayesian approach to tackle the unknown variable ordering issue. In particular, we utilize multiple maximum a posteriori estimates under the DAG-Wishart prior for each permutation, and subsequently construct the final estimate of the inverse covariance matrix. The proposed estimator has smaller variability and yields order-invariant property. We establish posterior convergence rates under mild assumptions and illustrate that our method outperforms existing approaches in estimating the inverse covariance matrices via simulation studies.     

Open Access Mandates and Policies: The Basics

Abstract

Universities can afford to subscribe to only a small percentage of available scholarly journals. Consequently, researchers do not have access to all articles in journals pertinent to their fields of study. The Open Access (OA) movement began in response to wanting to find a solution to this “serials crisis” and the availability of the Internet to disseminate scholarly research in new and innovative ways to a larger, worldwide audience. OA mandates are policies adopted by research institutions, universities, or funders that require researchers to provide free, unrestricted access to their published research by publishing in OA journals, depositing their articles in an OA repository or both. This edition of “In Lay Terms” provides basic information about OA mandates and policies, gives an overview of university and funder OA mandates, discusses geographic differences in policies and compliance rates, and reviews United States federal funding agencies’ OA policies and Plan S.     

On generalized conditional cumulative residual inaccuracy measure

Abstract

Recently, the notion of cumulative residual Rényi’s entropy has been proposed in the literature as a measure of information that parallels Rényi’s entropy. Motivated by this, here we introduce a generalized measure of it, namely cumulative residual inaccuracy of order α. We study the proposed measure for conditionally specified models of two components having possibly different ages called generalized conditional cumulative residual inaccuracy measure. Several properties of generalized conditional cumulative residual inaccuracy measure including the effect of monotone transformation are investigated. Further, we provide some bounds on using the usual stochastic order and characterize some bivariate distributions using the concept of conditional proportional hazard rate model.     

Spatial hidden Markov models and species distributions

A spatial hidden Markov model (SHMM) is introduced to analyse the distribution of a species on an atlas, taking into account that false observations and false non-detections of the species can occur during the survey, blurring the true map of presence and absence of the species. The reconstruction of the true map is tackled as the restoration of a degraded pixel image, where the true map is an autologistic model, hidden behind the observed map, whose normalizing constant is efficiently computed by simulating an auxiliary map. The distribution of the species is explained under the Bayesian paradigm and Markov chain Monte Carlo (MCMC) algorithms are developed. We are interested in the spatial distribution of the bird species Greywing Francolin in the south of Africa. Many climatic and land-use explanatory variables are also available: they are included in the SHMM and a subset of them is selected by the mutation operators within the MCMC algorithm.