时变系数空间自回归面板数据模型的极大似然估计

本文对扰动项存在跨时期的异方差、但不存在序列相关的时变系数空间自回归模型提出了极大似然的估计方法,并证明了该估计量的一致性,同时,证明了该估计量渐进服从正态分布,由此说明该估计量具有优良的大样本性质。同时,我们还对本文所提出估计量的小样本性质进行了数值模拟。本文研究表明,估计量虽然在N较小时偏差较大,但是随着N的不断增加,估计量偏差减小,体现了比较优良的渐进性质。同时,估计量的偏差会随着时期数的增加而变大,这说明本文所提出的估计方法适用于个体数较多、时期数较少的短面板数据。     

外商直接投资、集聚外部性与环境污染

本文在新经济地理和集聚外部性的综合理论框架下,利用2003-2013年中国275个地级市面板数据对外商直接投资、集聚外部性与环境污染之间的关系进行了实证研究。空间计量结果显示,我国城市污染排放强度存在显著的正向空间相关性,而且这种正向空间正相关性随着空间距离的增加呈现有规律的减弱趋势。在控制了FDI与集聚外部性的交互作用后,FDI引入明显降低了区域污染排放强度,Mar外部性和Jacobs外部性进一步增强了FDI的减排效应,但其影响效应随着地理距离的增加呈现出倒U型曲线特征。对于不同规模的城市而言,小城市更容易受益于专业化集聚带来的Mar外部性,大中型城市更容易分享多样化集聚带来的Jacobs外部性红利。     

多维视阈下国内职业指导研究的热点与趋势————基于共词分析和文献计量法

在就业形势日益严峻的背景下,如何促进就业成为社会热点,职业指导的关注度随之不断上升,因此,职业指导研究也就显得日益重要和紧迫。基于 837 篇关于职业指导的核心期刊文章,通过高频关键词词频分析、聚类分析、因子分析和社会网络分析,旨在探讨我国当前职业指导研究的热点,挖掘未来职业指导研究的潜在热点。研究发现,当前职业指导领域研究的热点是国外和本土职业指导理论研究、职业指导实践工作研究、多学科职业指导理论研究、创业教育和创业指导研究等;普通中小学的职业生涯教育、职业指导模式的变革与创新、关注就业能力和就业质量、中等职业学校职业指导研究、职业指导的专业化研究将成为未来职业指导研究的发展趋势。     

新会计准则中资产减值准备相关问题的思考

我国2007年1月1日起施行新的会计准则,其中<企业会计准则第8号--资产减值>从资产减值的确认、计量和相关信息等方面相对旧准则进行了一些改动.这种变动体现了我国会计准则与国际会计的接轨,并且进一步规范了上市公司的行为.本文对这些改动的内容及其影响进行了探讨,并提出了改进和完善的建议.     

成都环城市旅游带建设探索

提要:针对成都市境及周边地区假日与休闲旅游发展的无序状态,该文根据经济学的“点———轴”系统的理论原理,以及其在旅游发展中的应用前景,分析了成都及周边地区旅游资源空间分布特征、现状及问题,从重点开发的轴带和站点选择和分析入手提出构建成都地区环城市旅游带,在此基础上建设以成都市境为中心的旅游圈层,为成都近远郊旅游开发探索一条具规划意义的发展思路。     

Empirical likelihood test for equality of two distributions using distance of characteristic functions

In this paper, we investigate the problem of testing for the equality of two distributions. We employ a two-sample Jackknife Empirical Likelihood (JEL) approach to construct a test statistic whose limiting distribution is Chi-square distribution with degree of freedom 1, no matter what the data dimension (fixed) is. A variety of synthetic data experiments demonstrate that our JEL test statistic performs very well, with a very neat asymptotic distribution under the null hypothesis. Furthermore, we apply the test procedure to a real dataset to obtain competitive results.     

Some sequential selection procedures for good regression models

In the past decade a number of fixed sampling methods have been developed for selecting the "best" or at least a "good" subset of vaiable in regression analysis. We are interested in deriving a sequential selection procedure to select a subset of a random size including equations. Tables for an example are given at the end of this paper     

The Use of Switching Point and Protection Levels to Improve Revenue Performance in Order‐Driven Production Systems

In a multiproduct order‐driven production system, an organization has to decide how to selectively accept orders and allocate capacity to these orders so as to maximize total profit (TP). In this article, we incorporate the novel concept of switching point in developing three capacity‐allocation with switching point heuristics (CASP_a‐c). Our analysis indicates that all three CASP heuristics outperform the first‐come‐first‐served model and Barut and Sridharan's dynamic capacity‐allocation process (DCAP) model. The best model, CASP_b, has an 8% and 6% average TP improvement over DCAP using the split lot and whole lot policies, respectively. In addition, CASP_b performs particularly well under operating conditions of tight capacity and large price differences between product classes. The introduction of a switching point, which has not been found in previous capacity‐allocation heuristics, provides for a better balance between forward and backward allocation of available capacity and plays a significant role in improving TP.     

Lp convergence and complete convergence for weighted sums of AANA random variables

Let {X_n, n ? 1} be a sequence of asymptotically almost negatively associated (AANA, for short) random variables which is stochastically dominated by a random variable X, and {d_ni, 1 ? i ? n, n ? 1} be a sequence of real function, which is defined on a compact set E. Under some suitable conditions, we investigate some convergence properties for weighted sums of AANA random variables, especially the L^p convergence and the complete convergence. As an application, the Marcinkiewicz–Zygmund-type strong law of large numbers for AANA random variables is obtained.     

False discovery rates for large-scale model checking under certain dependence

In many scientific fields, it is interesting and important to determine whether an observed data stream comes from a prespecified model or not, particularly when the number of data streams is of large scale, where multiple hypotheses testing is necessary. In this article, we consider large-scale model checking under certain dependence among different data streams observed at the same time. We propose a false discovery rate (FDR) control procedure to check those unusual data streams. Specifically, we derive an approximation of false discovery and construct a point estimate of FDR. Theoretical results show that, under some mild assumptions, our proposed estimate of FDR is simultaneously conservatively consistent with the true FDR, and hence it is an asymptotically strong control procedure. Simulation comparisons with some competing procedures show that our proposed FDR procedure behaves better in general settings. Application of our proposed FDR procedure is illustrated by the StarPlus fMRI data.