大学生村官工作绩效影响因素与改进策略探讨

大学生村官工作具有综合、琐碎、繁杂、服务面广、服务群体多等特点,持续提高大学生村官工作绩效须狠抓制度建设,从完善招考机制、健全岗前培训机制、优化和完善工作机制、完善绩效考评和激励保障机制、建立健全流动机制、实施离任村官帮扶计划等关键环节着手。只有进一步推进体制革新,促进各部门发挥优势,形成合力,才能持续提高大学生村官的工作绩效,为破解“三农”问题、缩小城乡差距,进而推进城乡发展一体化作出更大贡献。     

财政政策DSGE模型中国化构建及其应用

事前模拟经济对财政政策变化的反应是检验政策效果的重要手段。本文通过国外经典模型中国化改进,并引入系统财政规则,构建财政政策DSGE模型。在有效税率估算校准与参数贝叶斯估计基础上,给出政策模拟检验应用示例。发现税率冲击效应模拟是税制改革实验的有效方法,资本税率可作为经济结构调整的政策工具,以及当前增加政府支出拉动增长作用微弱等结论。该研究也可为我国DSGE模型研究提供参考。     

需求与回应:一项关于流动儿童适应的再研究

人类有潜能帮助其自动地调适以寻求与环境的平衡,但人类的天赋与环境资源供给的相互作用才是增进人类适应的最终因素。流动儿童在与环境的互动中,资源供给欠缺,影响其适应。为了回应流动儿童的需求,需要从环境层面输入资源,培养其积极的自我认知与情绪体验,帮助其适应以寻求与环境的平衡。由于城乡间的差异给流动儿童带来的不适应、断裂及社会排斥,持续性的多系统整合介入是帮助流动儿童适应的重要因素。     

通货膨胀的宏观经济影响因素分析

本文首先利用贝叶斯向量自回归（BVAR）模型,分析了通货膨胀对宏观经济的冲击响应及其剧烈程度。然后,利用门限模型验证了通货膨胀在不同变量作为门限变量情况下的门限效应。结果表明：通货膨胀对六个因素冲击的反应程度各异,其中,对流动性过剩的反应程度最强,对股票价格、产出缺口和国际油价的反应适度,对实际有效汇率和房价的反应较弱;股票价格、汇率和国际油价具有明显的门限特征,它们分别将通货膨胀分为高低两种区制状态。以上结果有利于我们更好地认识通货膨胀的反应机制,采用合理的经济政策应对通货膨胀。     

Variance estimation in model assisted survey sampling

Two versions of Yates-Grundy type variance estimators are usually employed for large samples when estimating a survey population total by a generalized regression (Greg, in brief) predictor motivated by consideration of a linear regression model. Their two alternative modifications are developed so that the limiting values of the design expectations of the model expectations of variance estimators 'match' respectively the (I) model expectations of the Taylor approximation of the design variance of the Greg predictor and the (II) limiting value of the design expectation of the model expectation of the squared difference between the Greg predictor and the population total. The exercise is extended to yield modifications needed when randomized response (RR) is only available rather than direct response (DR) when one encounters sensitive issues demanding protection of privacy. A comparative study based on simulation is presented for illustration..     

Conditions of Slope-Rotatability for Third-Order Polynomial Regression Models

In experimental design for response surface analysis, it is sometimes of interest to estimate the difference of responses at two points. If differences at points close together are involved, the design that reliably estimates the slope of the response surface is important. In particular, Hader and Park (1978 Hader , R. J. , Park , S. H. ( 1978 ). Slope-rotatable central composite designs . Technometrics 20 : 413 – 417 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) suggested the concept of slope-rotatability and studied slope rotatable central composite designs. Until now, many response surface designs including central composite designs have been suggested for fitting second order response surface models. However, we often need to fit third-order polynomial regression models. In this article, we suggest extended central composite designs (ECCDs) to fit third-order models and find the necessary and sufficient conditions for slope-rotatability over all directions in the third-order polynomial models.     

Robust Model-Based Inference for Incomplete Data via Penalized Spline Propensity Prediction

Parametric model-based regression imputation is commonly applied to missing-data problems, but is sensitive to misspecification of the imputation model. Little and An (2004 Little , R. J. A. , An , H. ( 2004 ). Robust likelihood-based analysis of multivariate data with missing values . Statistica Sinica 14 : 949 – 968 .[Web of Science ®] , [Google Scholar]) proposed a semiparametric approach called penalized spline propensity prediction (PSPP), where the variable with missing values is modeled by a penalized spline (P-Spline) of the response propensity score, which is logit of the estimated probability of being missing given the observed variables. Variables other than the response propensity are included parametrically in the imputation model. However they only considered point estimation based on single imputation with PSPP. We consider here three approaches to standard errors estimation incorporating the uncertainty due to non response: (a) standard errors based on the asymptotic variance of the PSPP estimator, ignoring sampling error in estimating the response propensity; (b) standard errors based on the bootstrap method; and (c) multiple imputation-based standard errors using draws from the joint posterior predictive distribution of missing values under the PSPP model. Simulation studies suggest that the bootstrap and multiple imputation approaches yield good inferences under a range of simulation conditions, with multiple imputation showing some evidence of closer to nominal confidence interval coverage when the sample size is small.     

Comparing Pearson Correlations: Dealing with Heteroscedasticity and Nonnormality

Methods for computing a confidence interval for the difference between two Pearson correlations are compared when dealing with nonnormality and heteroscedasticity. Variations of a method derived by Zou performed relatively well in simulations for the dependent case. For the independent case, the Wilcox–Muska method performed best.     

A Comparison of Estimation Methods on the Coverage Probability of Satterthwaite Confidence Intervals for Assay Precision with Unbalanced Data

Construction of closed-form confidence intervals on linear combinations of variance components were developed generically for balanced data and studied mainly for one-way and two-way random effects analysis of variance models. The Satterthwaite approach is easily generalized to unbalanced data and modified to increase its coverage probability. They are applied on measures of assay precision in combination with (restricted) maximum likelihood and Henderson III Type 1 and 3 estimation. Simulations of interlaboratory studies with unbalanced data and with small sample sizes do not show superiority of any of the possible combinations of estimation methods and Satterthwaite approaches on three measures of assay precision. However, the modified Satterthwaite approach with Henderson III Type 3 estimation is often preferred above the other combinations.     

Developments in Maximum Likelihood Unit Root Tests

The exact maximum likelihood estimate provides a test statistic for the unit root test that is more powerful than the usual least-squares approach. In this article, a new derivation is given for the asymptotic distribution of this test statistic that is simpler and more direct than the previous method. The response surface regression method is used to obtain a fast algorithm that computes accurate finite-sample critical values. This algorithm is available in the R package mleur that is available on CRAN. The empirical power of the new test is shown to be much better than the usual test not only in the normal case but also for innovations generated from an infinite variance stable distribution as well as for innovations generated from a GARCH(1,1) process.