中国制造业碳排放的驱动因素及脱钩效应

 本文运用改进的拉氏因素分解法,对中国制造业1995-2007年碳排放的驱动因素进行了研究,并基于DPSIR框架构建了碳排放脱钩指数,对制造业部门碳排放的脱钩效应进行了测度。研究结果表明我国制造业碳排放的驱动因素有着较强的阶段性特点;产出效应为主要的正向驱动因素,能源强度效应为主要的负向驱动因素;制造业部门碳排放存在一定的脱钩效应,但强脱钩年份较少;在强脱钩向弱脱钩的转变过程中,经济结构效应起着关键作用。本文据此给出了若干政策建议。     

Packing 5-cycles into balanced complete m-partite graphs for odd m

Let be a complete m-partite graph with partite sets of sizes n ₁,n ₂,…,n _m . A complete m-partite graph is balanced if each partite set has n vertices. We denote this complete m-partite graph by K _m(n). In this paper, we completely solve the problem of finding a maximum packing of the balanced complete m-partite graph K _m(n), m odd, with edge-disjoint 5-cycles and we explicitly give the minimum leaves. Dedicated to Professor Frank K. Hwang on the occasion of his 65th birthday. Research of M.-H.W. was supported by NSC 93-2115-M-264-001.     

我国城镇居民收入不平等变动实证研究 ——基于收入分布变化分解的视角

 对收入不平等变动研究,目前多数基于对不平等指标的分解,计算结果会因收入数据分组的不同而产生较大差异。本文基于收入分布变化分解的视角,对我国地级市以上城镇居民收入不平等变动进行了实证分析。研究结果表明,收入分布位置变化对于收入分布变化的影响最大,而收入分布形状变化却在一定程度上抵消了这种影响效果,收入分布的其它变化虽有影响,但程度相对较低。收入分布位置变化有助于缓解不平等程度的加剧,而收入分布形状变化则加速不平等程度的恶化,其影响程度对于不同的收入阶层有所不同。本文认为,经济增长是缓解收入不平等恶化的主要原因,而收入分配的改变却在一定程度上加剧了收入不平等。     

样本选择模型截距项的半参数估计及应用———户籍工资差异分解研究

样本选择模型是解决样本选择问题的主要工具,广泛应用于工资差异分解、平均处理效应测算等实证研究。截距项的估计是样本选择模型半参数估计中相对独立且重要的一部分,现有的以无穷处识别为代表的半参数估计方法存在窗宽参数难以选取的问题。为此,本文把无穷处识别等价转化为边界处识别,并基于新的识别关系给出样本选择模型截距项的核估计方法。这种新方法的好处在于将样本选择模型截距项的估计纳入核估计框架中,从而可以采用经验法则解决现有方法的窗宽选取难题。数值模拟结果表明,本文所提出的估计方法在不同设定下均有良好的有限样本表现。把这种新的半参数估计方法应用于户籍工资差异分解后发现,我国劳动力市场目前不存在明显的户籍差别待遇。     

Average run length comparison of multivariate control charts

In this paper we use Monte Carlo Simulation methodology to compare the effectiveness of five multivariate quality control methods, namely Hotelling T ², Multivariate Shewhart Char, Discriminant Analysis, Decomposition Method, and Multivariate Ridge Residual Chart-developed by Authors-, for controlling the mean vector in a multivariate process. P-dimensional multivariate normal data generated using different covariance structures. Various amount of shift in the mean vector is induced and the resulting Average Run Length (ARL) is computed. The effectiveness of each method with regard to ARL is discussed.     

Non-oriented slack-based decompositions of profit change with an application to Taiwanese banking

A change in profit can originate from the output side and the input side. In the spirit of work by Tone [1] and follows Grifell-Tatjé and Lovell's [2], we propose a non-oriented slacks-based measure (SBM) model to decompose the change in the operating profit into various meaningful components: quantity effect and a price effect. The quantity effect can be decomposed into a productivity effect and an activity effect. The productivity effect is further decomposed into a technical effect and an operating efficiency effect. Both of them include an output side, which will result in a change in revenue and an input side which will result in a change in cost. The activity effect can be decomposed into a product mix effect, a resource mix effect and a scale effect. We illustrate our decompositions to the Taiwanese banking sector during the period 1994-2002 using the average of the base and current prices to evaluate these contributions. We find ignoring input side effects on the decomposition of profit changes would cause misleading results in managerial issues.     

Performance Analysis of Exponential Multi-Server Production Lines with Fluid Flow and Finite Buffers

This article presents an approximation method for fluid flow production lines with multi-server workstations and finite buffers. Each workstation consists of parallel identical servers, which are subject to operation-dependent failures with exponentially distributed uptimes and downtimes. The proposed method decomposes the production line into single-buffer subsystems, each described by a continuous state Markov process, the parameters of which are determined iteratively. The approximation method is appropriate for the analysis of longer production lines, able to accurately estimate performance characteristics (e.g., throughput and mean buffer content), and shown to perform well on a large test set.     

Two-Term Edgeworth Expansions for the Classes of U- and V-statistics

Much effort has been devoted to deriving Edgeworth expansions for various classes of statistics that are asymptotically normally distributed, with derivations tailored to the individual structure of each class. Expansions with smaller error rates are needed for more accurate statistical inference. Two such Edgeworth expansions are derived analytically in this paper. One is a two-term expansion for the standardized U-statistic of order m, m ? 3, with an error rate o(n^{? 1}). The other is an expansion with the same error rate for the distribution of the standardized V-statistic of the same order. In deriving the Edgeworth expansion, we made use of the close connection between the V- and U-statistics, which permits to first derive the needed expansion for the related U-statistic, then extend it to the V-statistic, taking into consideration the estimation of all difference terms between the two statistics.     

全球中小企业反规制进程及其因素分解

 20世纪70年代以后，一股世界性的“反规制”的潮流逐渐形成。“反规制”是指对过度规制的矫正，是为了更有效地发挥市场机制的作用而放松或改进政府对企业监管的行为。本文首先对目前全球中小企业的规制现状进行了统计分析；然后从对全球中小企业反规制进程及其国别差异问题进行了定量研究；最后建立面板数据计数模型，对影响全球中小企业反规制进程的诸因素及其影响力进行了系统的分解和测算。     

New Developments in Statistical Computing

Many users of regression methods are attracted to the notion that it would be valuable to determine the relative importance of independent variables. This article demonstrates a method based on hierarchies that builds on previous efforts to decompose R ² through incremental partitioning. The standard method of incremental partitioning has been to follow one order among the many possible orders available. By taking a hierarchical approach in which all orders of variables are used, the average independent contribution of a variable is obtained and an exact partitioning results. Much the same logic is used to divide the joint effect of a variable. The method is general and applicable to all regression methods, including ordinary least squares, logistic, probit, and log-linear regression. A validation test demonstrates that the algorithm is sensitive to the relationships in the data rather than the proportion of variability accounted for by the statistical model used.