十六届三中全会后我国所有制问题最新研究综述

党的十六届三中全会提出,“要大力发展混合所有制经济,使股份制成为公有制的主要实现形式”,引发经济学界对所有制问题展开了激烈的争论。学界对混合所有制经济提出许多不同的理解,对股份制企业的所有制性质作出许多不同的判断。鉴于此,本文对十六届三中全会以来国内对所有制问题的争论作一个简要的综述,以反映我国所有制问题研究的最新进展。     

论多种所有制经济在“创新驱动”中协同发展

在当前严峻的国内外经济政治形势中,我国必须加快实施创新驱动发展战略,以自主创新为核心驱动力,引导各种所有制经济共同致力于创新驱动经济结构转型升级,继续发挥好国有经济在创新升级中的主导作用。同时也必须正确认识,占据国民经济半壁江山的民营经济能够顺利实现转型升级,是我国经济结构转型升级的关键。因而,通过深化体制改革、机制创新来加快各种所有制经济的结构优化,充分激发各种所有制经济主体的创新发展,尤其是民营经济的创新升级,显得尤其迫切与必要。同时,应把混合所有制经济形式作为"开放式"创新的重要微观载体,引导外资企业积极参与到我国创新驱动发展战略之中。     

金融自由化浪潮下我国金融业发展对策

20世纪70年代以来,国际金融自由化的进程明显加快,金融工具不断创新,金融信息化蓬勃发展.利率市场化、放松金融管制、开放国内资本市场、加强对全球金融的统一监管成为大势所趋.文章通过国际金融自由化进程中一些规律性经验的总结及我国金融改革历程的回顾,对我国金融市场在利率、外汇及证券等业务的开放提出了建议.     

Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations

Summary.  Structured additive regression models are perhaps the most commonly used class of models in statistical applications. It includes, among others, (generalized) linear models, (generalized) additive models, smoothing spline models, state space models, semiparametric regression, spatial and spatiotemporal models, log-Gaussian Cox processes and geostatistical and geoadditive models. We consider approximate Bayesian inference in a popular subset of structured additive regression models, latent Gaussian models , where the latent field is Gaussian, controlled by a few hyperparameters and with non-Gaussian response variables. The posterior marginals are not available in closed form owing to the non-Gaussian response variables. For such models, Markov chain Monte Carlo methods can be implemented, but they are not without problems, in terms of both convergence and computational time. In some practical applications, the extent of these problems is such that Markov chain Monte Carlo sampling is simply not an appropriate tool for routine analysis. We show that, by using an integrated nested Laplace approximation and its simplified version, we can directly compute very accurate approximations to the posterior marginals. The main benefit of these approximations is computational: where Markov chain Monte Carlo algorithms need hours or days to run, our approximations provide more precise estimates in seconds or minutes. Another advantage with our approach is its generality, which makes it possible to perform Bayesian analysis in an automatic, streamlined way, and to compute model comparison criteria and various predictive measures so that models can be compared and the model under study can be challenged.     

ESTIMATING A PARAMETER WHEN IT IS KNOWN THAT THE PARAMETER EXCEEDS A GIVEN VALUE

In some statistical problems a degree of explicit, prior information is available about the value taken by the parameter of interest, θ say, although the information is much less than would be needed to place a prior density on the parameter's distribution. Often the prior information takes the form of a simple bound, ‘θ > θ₁ ’ or ‘θ < θ₁ ’, where θ₁ is determined by physical considerations or mathematical theory, such as positivity of a variance. A conventional approach to accommodating the requirement that θ > θ₁ is to replace an estimator, , of θ by the maximum of and θ₁. However, this technique is generally inadequate. For one thing, it does not respect the strictness of the inequality θ > θ₁ , which can be critical in interpreting results. For another, it produces an estimator that does not respond in a natural way to perturbations of the data. In this paper we suggest an alternative approach, in which bootstrap aggregation, or bagging, is used to overcome these difficulties. Bagging gives estimators that, when subjected to the constraint θ > θ₁ , strictly exceed θ₁ except in extreme settings in which the empirical evidence strongly contradicts the constraint. Bagging also reduces estimator variability in the important case for which is close to θ₁, and more generally produces estimators that respect the constraint in a smooth, realistic fashion.     

SEMIPARAMETRIC REGRESSION AND GRAPHICAL MODELS

Semiparametric regression models that use spline basis functions with penalization have graphical model representations. This link is more powerful than previously established mixed model representations of semiparametric regression, as a larger class of models can be accommodated. Complications such as missingness and measurement error are more naturally handled within the graphical model architecture. Directed acyclic graphs, also known as Bayesian networks, play a prominent role. Graphical model-based Bayesian 'inference engines', such as bugs and vibes , facilitate fitting and inference. Underlying these are Markov chain Monte Carlo schemes and recent developments in variational approximation theory and methodology.     

自助多水平统计模型在代际收入流动研究中的应用

文章首先对代际收入流动的相关文献进行简单回顾,在此基础上运用自助法多水平统计模型对不同年份的CHNS数据进行分析。结果表明在控制了个体属性变量后,农村家庭子女收入受户主收入影响,即表现出一定程度的代际流动性;文章的结果同时表明农村地区代际流动性不存在中部、东部以及西部之间地域上的差别。最后,文章指出研究中的一些不足之处,指出了未来研究方向。     

Change-Point Tests for the Error Distribution in Non-parametric Regression

Abstract.  Several testing procedures are proposed that can detect change-points in the error distribution of non-parametric regression models. Different settings are considered where the change-point either occurs at some time point or at some value of the covariate. Fixed as well as random covariates are considered. Weak convergence of the suggested difference of sequential empirical processes based on non-parametrically estimated residuals to a Gaussian process is proved under the null hypothesis of no change-point. In the case of testing for a change in the error distribution that occurs with increasing time in a model with random covariates the test statistic is asymptotically distribution free and the asymptotic quantiles can be used for the test. This special test statistic can also detect a change in the regression function. In all other cases the asymptotic distribution depends on unknown features of the data-generating process and a bootstrap procedure is proposed in these cases. The small sample performances of the proposed tests are investigated by means of a simulation study and the tests are applied to a data example.     

反思与重构:对结合犯概念的再思考

对结合犯概念的揭示应注意以下几个方面:不能脱离结合犯的初始意义,应反映结合犯的本质内涵和立法目的,不能脱离法律的实证规定,不能混淆规范刑法学与本体刑法学的界限,不能脱离犯罪成立的理论体系.所谓结合犯是指立法者基于特定立法目的的考虑,将两个具有内在联系性的犯罪(构成要件)规定为独立犯罪(构成要件)的情况.     

汉英混用的成因及语用价值探析

论文分析了汉英混用的成因：方便交流,更具权威性;形式简洁、通用性强;追求流行,标新立异。以及汉英混用的娱乐性、简洁性、委婉性、广泛性等语用价值。