国外减税实践及其启示

减税是世界各国促进经济稳定增长的重要手段。总结与借鉴国外几十年来减税实践的经验与教训,理性分析我国当前经济运行走势及减税可行性,充分发挥税收政策在启动有效需求、改善有效供给中的作用,是十分必要和有益的。     

THE REDUCED-RANK GROWTH CURVE MODEL FOR DISCRIMINANT ANALYSIS OF LONGITUDINAL DATA

This paper presents a method of discriminant analysis especially suited to longitudinal data. The approach is in the spirit of canonical variate analysis (CVA) and is similarly intended to reduce the dimensionality of multivariate data while retaining information about group differences. A drawback of CVA is that it does not take advantage of special structures that may be anticipated in certain types of data. For longitudinal data, it is often appropriate to specify a growth curve structure (as given, for example, in the model of Potthoff & Roy, 1964). The present paper focuses on this growth curve structure, utilizing it in a model-based approach to discriminant analysis. For this purpose the paper presents an extension of the reduced-rank regression model, referred to as the reduced-rank growth curve (RRGC) model. It estimates discriminant functions via maximum likelihood and gives a procedure for determining dimensionality. This methodology is exploratory only, and is illustrated by a well-known dataset from Grizzle & Allen (1969).     

王蒙小说还原生活的艺术取向——王蒙小说的生成前提

王蒙小说是一种生活化小说,他的小说创作,既看重"艺术直觉"、艺术联想,同时又强调生活,这是王蒙小说生成的前提,这种小说生成的思维方式又影响了他的文本形式,也促成了王蒙小说文体的多样化,从而使其小说从文本形式到精神内质都向生活全面开放.     

关于“新批评派”成员构成的几点认识

说到“新批评派”的成员构成,国内外学界有许多不同理解.这些认识,虽不乏真知灼见,但也不乏随意甚至混乱之嫌.为进一步推进国内“新批评”研究,文章提出,“新批评派”本身复杂丰富,须分人、分层、分国籍考察;对“新批评派”成员的译介须统一译名,避免混淆而用全称;祛除对“新批评派”单质化和标签化理解,还原其丰富性和复杂性.     

Shorter hours wanted? A systematic review of working-time preferences and outcomes

Changes in working time have important economic, social and environmental implications. However, research on workers' preferences is highly fragmented across disciplines and journals. To overcome this, the present article provides a systematic review of the literature, analysing 173 studies. The study shows that working-time outcomes and mismatches are mostly studied in a small group of OECD countries, using survey data and statistical methods. The most clear and consistent results are about gender, care responsibilities and current working hours. Among professions, only the medical workforce receives substantial attention. Norms and preference formation are poorly understood, and more qualitative research is essential to understand how working-time reductions could spread.     

我国资本市场CDR制度选择与现行证券法律体系的挑战

证券存托凭证(DR)的推行对于创新投资渠道,扩大融资空间,促进证券市场渐进发展有着重要的作用.但是发行CDR(中国证券存托凭证)是国内资本市场开放的一项系统工程,牵涉到法律、银行服务、金融监管等方方面面的问题.考察国外证券市场的成熟经验,以严格监管为特色的ADR(Ⅲ级别)作为CDR的发行模式比较适合中国的目前证券市场发展的现状.由于证券存托凭证作为一种金融衍生工具,在法律上缺乏明确的法律关系定位,特别是与现行的证券法律体系存在诸多制度矛盾与障碍,上述种种问题都急需通过后续的证券法修改来加以完善.     

Sliced Average Variance Estimation for Censored Data

We consider a nonlinear censored regression problem with a vector of predictors. With censoring, high-dimensional regression analysis becomes much more complicated. Since censoring can cause severe bias in estimation, modification to adjust such bias is needed to be made. Based on the weight adjustment, we develop the modification of sliced average variance estimation for estimating the lifetime central subspace without requiring a prespecified parametric model. Our proposed method preserves as much regression information as possible. Simulation results are reported and comparisons are made with the sliced inverse regression of Li et al. (1999 Li , K. C. , Wang , J. L. , Chen , C. H. ( 1999 ). Dimension reduction for censored regression data . Ann. Statist. 27 : 1 – 23 . [Google Scholar]).     

On the Bias of the Maximum Likelihood Estimator for the Two-Parameter Lomax Distribution

The Lomax (Pareto II) distribution has found wide application in a variety of fields. We analyze the second-order bias of the maximum likelihood estimators of its parameters for finite sample sizes, and show that this bias is positive. We derive an analytic bias correction which reduces the percentage bias of these estimators by one or two orders of magnitude, while simultaneously reducing relative mean squared error. Our simulations show that this performance is very similar to that of a parametric bootstrap correction based on a linear bias function. Three examples with actual data illustrate the application of our bias correction.     

A Compressive Sensing Based Analysis of Anomalies in Generalized Linear Models

In this article, we present a compressive sensing based framework for generalized linear model regression that employs a two-component noise model and convex optimization techniques to simultaneously detect outliers and determine optimally sparse representations of noisy data from arbitrary sets of basis functions. We then extend our model to include model order reduction capabilities that can uncover inherent sparsity in regression coefficients and achieve simple, superior fits. Second, we use the mixed ?₂/?₁ norm to develop another model that can efficiently uncover block-sparsity in regression coefficients. By performing model order reduction over all independent variables and basis functions, our algorithms successfully deemphasize the effect of independent variables that become uncorrelated with dependent variables. This desirable property has various applications in real-time anomaly detection, such as faulty sensor detection and sensor jamming in wireless sensor networks. After developing our framework and inheriting a stable recovery theorem from compressive sensing theory, we present two simulation studies on sparse or block-sparse problems that demonstrate the superior performance of our algorithms with respect to (1) classic outlier-invariant regression techniques like least absolute value and iteratively reweighted least-squares and (2) classic sparse-regularized regression techniques like LASSO.     

Generalized Jackknife-Based Estimators for Univariate Extreme-Value Modeling

In this article, we revisit the importance of the generalized jackknife in the construction of reliable semi-parametric estimates of some parameters of extreme or even rare events. The generalized jackknife statistic is applied to a minimum-variance reduced-bias estimator of a positive extreme value index—a primary parameter in statistics of extremes. A couple of refinements are proposed and a simulation study shows that these are able to achieve a lower mean square error. A real data illustration is also provided.