契约关系不健全情况下国有企业的组织失效

文章把国有企业的责任体系失效归结为企业的组织失效。组织失效表现为组织无法有效地履行其主要功能,包括组织目标功能的失效和组织调控功能的失效。在企业目标功能失效的情况下,企业调控功能失效可以为偏离企业目标的个人目标提供更多的实现机会。国有企业组织功能失效的原因是企业治理结构的失效,国有企业契约关系的失效是治理结构失效的症结。契约失效首先使国有企业的目标功能失效,进而使企业的内部调控功能失效。契约失效除了使法人治理结构失效外,还对企业带来较高的制度性成本。     

"形式"的解放:洞见与盲视--胡适的"白话"主张和"新诗"理论

从胡适和梅光迪、任鸿隽的两次争论入手,对胡适弃文言用白话的立论依据进行了历史和理论的梳理.胡适从思考中国文学"有文无质"的弊病出发,为"白话"文学凝聚了"说法",这是一种把文学变革落实到语言形式的革新方案,但胡适把白话的"达意"直接移用到"新诗"时,混淆了"诗法"和"文法"的区分.     

Exact confidence interval estimation for the difference in diagnostic accuracy with three ordinal diagnostic groups

In the cases with three ordinal diagnostic groups, the important measures of diagnostic accuracy are the volume under surface (VUS) and the partial volume under surface (PVUS) which are the extended forms of the area under curve (AUC) and the partial area under curve (PAUC). This article addresses confidence interval estimation of the difference in paired VUS s and the difference in paired PVUS s. To focus especially on studies with small to moderate sample sizes, we propose an approach based on the concepts of generalized inference. A Monte Carlo study demonstrates that the proposed approach generally can provide confidence intervals with reasonable coverage probabilities even at small sample sizes. The proposed approach is compared to a parametric bootstrap approach and a large sample approach through simulation. Finally, the proposed approach is illustrated via an application to a data set of blood test results of anemia patients.     

Employment Policy,the Crowding‐out Effect and Imperfect Competition

This paper presents a macroeconomic model with imperfect competition in the commodity market, and uses it to address how the commodity market’s structure is related to the efficacy of government employment policies. It is found that job creation in the public sector may lead to a decrease in output and an increase in prices. In particular, these adverse side‐effects will be alleviated when competition in the goods market is less perfect. We also find that public‐sector job creation definitely has a positive effect on total employment, though it may crowd out private‐sector employment.     

公共财政视角下的社会事业科技——基于成都实践的思考

社会事业科技是相对服务于生产和经济发展而言的科技。文章在分析当前社会科技发展呈现的趋势基础上指出了成都市社会事业科技发展中存在的主要问题：社会科技尚未成为独立的科研领域;科技经费来源单一;科技投入结构不合理;人才结构不合理,流失严重;产出效益难以评估。并据此提出了5条对策建议：明确公共财政的分类标准,建立公共财政科技投入模式;充分发挥市场的调配作用,形成多层次的科技投入格局;运用各种财政手段和“拟成果购买制”构建多元化的科技投入格局;充分利用资本市场,积极吸纳国内外各种科技贷款和民间科技捐助,并建立多渠道的风险投资机制;构建社会科技公共研发平台。     

Estimation of treatment effects for heterogeneous matched‐pairs data with probit models

Estimating the effect of medical treatments on subject responses is one of the crucial problems in medical research. Matched‐pairs designs are commonly implemented in the field of medical research to eliminate confounding and improve efficiency. In this article, new estimators of treatment effects for heterogeneous matched‐pairs data are proposed. Asymptotic properties of the proposed estimators are derived. Simulation studies show that the proposed estimators have some advantages over the famous Heckman's estimator, the conditional maximum likelihood estimator, and the inverse probability weighted estimator. We apply the proposed methodology to a data set from a study of low‐birth‐weight infants.     

Stable feature screening for ultrahigh dimensional data

This paper is concerned with the stable feature screening for the ultrahigh dimensional data. To deal with the ultrahigh dimensional data problem and screen the important features, a set-averaging measurement is proposed. The model averaging technique and the conditional quantile method are used to construct the weighted set-averaging feature screening procedure to identify the relationships between the possible predictors and the response variable. The proposed screening method is model free, stable and possesses the sure screening property under some regular conditions. Some Monte Carlo simulations and a real data application are conducted to evaluate the performance of the proposed procedure.     

New Robust Variable Selection Methods for Linear Regression Models

Motivated by an entropy inequality, we propose for the first time a penalized profile likelihood method for simultaneously selecting significant variables and estimating unknown coefficients in multiple linear regression models in this article. The new method is robust to outliers or errors with heavy tails and works well even for error with infinite variance. Our proposed approach outperforms the adaptive lasso in both theory and practice. It is observed from the simulation studies that (i) the new approach possesses higher probability of correctly selecting the exact model than the least absolute deviation lasso and the adaptively penalized composite quantile regression approach and (ii) exact model selection via our proposed approach is robust regardless of the error distribution. An application to a real dataset is also provided.     

Partially linear single-index model with missing responses at random

This paper considers semiparametric partially linear single-index model with missing responses at random. Imputation approach is developed to estimate the regression coefficients, single-index coefficients and the nonparametric function, respectively. The imputation estimators for the regression coefficients and single-index coefficients are obtained by a stepwise approach. These estimators are shown to be asymptotically normal, and the estimator for the nonparametric function is proved to be asymptotically normal at any fixed point. The bandwidth problem is also considered in this paper, a delete-one cross validation method is used to select the optimal bandwidth. A simulation study is conducted to evaluate the proposed methods.     

Preliminary test estimation for the second order autoregression

Consequences of preliminary test model identification procedures in time series analysis are examined in the context of a squared error risk function. Asymptotic risk comparisons and Monte Carlo studies are used in comparing these procedures.