对我国当前货币政策的分析

文章从理论依据和现实考察两方面分析货币政策的操作效果,并对今后我国货币政策的操作方向提出了若干建议.     

聚焦中国英语课堂:影响学生参与讨论的因素—— 一次采用定性分析法进行本土化研究的尝试

通过课堂观察及课后的学生采访,本文采用定性研究法考察了决定英语课堂上中国学生讨论情况的因素。　　本文的主要发现是：（１） 中国文化推崇的谦虚美德明显地妨碍了学生们抒发己见;（２） 学生推理能力和英语表达能力之间的鸿沟是引起表达焦虑的主要原因;（３） 教师的角色左右学生讨论中表现。基于上述发现,本文认为,课堂学习由民族性格、教育体制、口语习得心理以及教师角色所决定。因此建议,今后要注意研究课堂学习和其他影响因素之间的关系     

球墨铸铁曲轴的气体软氮化

柴油机的珠墨铸铁曲轴系重要零件,气体软氮化应用到球墨铸铁领域还是一种新工艺,而且比中频淬火效果好,提高了曲轴的抗疲劳和抗磨损的能力。本文对气体软氮化的基本原理、设备、工艺参数的选择和催渗方法进行了探讨,对软氮化后曲轴的尺寸形状误差、光洁度和机械性能的提高等进行了较详细的试验、分析和比较,结果,经气体软氮化后的曲轴不但尺寸形站误差符合图纸要求外,而且抛去轻微氧化层后光洁度还略有提高,曲轴安全系数由1.3～1.6提高到1.70～1.77,并且一般不再发生疲劳断(?),使用寿命比原来寿命增加3.32倍以上。     

政府生产性基础设施投资对农业经济增长的影响——基于西安市的实证研究

根据西安市1988—2006年间的数据建立柯布一道格拉斯生产函数,在自回归分布滞后模型（ADL）下用最小二乘法（OLS）进行估计。实证分析结果显示：政府基础设施投资对西安市农业经济增长贡献显著,并且其作用远大于非基础设施投资的贡献,表明政府的生产性基础设施投资是西安市农业经济快速增长的关键因素。在此基础上,对西安市政府近期进行基础设施投资的重点方向提出了相关的政策建议。     

Three-part model for fractional response variables with application to Chinese household health insurance coverage

A survey on health insurance was conducted in July and August of 2011 in three major cities in China. In this study, we analyze the household coverage rate, which is an important index of the quality of health insurance. The coverage rate is restricted to the unit interval [0, 1], and it may differ from other rate data in that the “two corners” are nonzero. That is, there are nonzero probabilities of zero and full coverage. Such data may also be encountered in economics, finance, medicine, and many other areas. The existing approaches may not be able to properly accommodate such data. In this study, we develop a three-part model that properly describes fractional response variables with non-ignorable zeros and ones. We investigate estimation and inference under two proportional constraints on the regression parameters. Such constraints may lead to more lucid interpretations and fewer unknown parameters and hence more accurate estimation. A simulation study is conducted to compare the performance of constrained and unconstrained models and show that estimation under constraint can be more efficient. The analysis of household health insurance coverage data suggests that household size, income, expense, and presence of chronic disease are associated with insurance coverage.     

Generation of multivariate distributions by vertical density representation

Troutt (1991,1993) proposed the idea of the vertical density representation (VDR) based on Box-Millar method. Kotz, Fang and Liang (1997) provided a systematic study on the multivariate vertical density representation (MVDR). Suppose that we want to generate a random vector X[d]Rⁿthat has a density function ?(x). The key point of using the MVDR is to generate the uniform distribution on [D]_?(v) = {x :?(x) = v} for any v > 0 which is the surface in RⁿIn this paper we use the conditional distribution method to generate the uniform distribution on a domain or on some surface and based on it we proposed an alternative version of the MVDR(type 2 MVDR), by which one can transfer the problem of generating a random vector X with given density f to one of generating (X, X_n+i) that follows the uniform distribution on a region in Rⁿ⁺¹defined by ?. Several examples indicate that the proposed method is quite practical.     

Statistical inference on asymptotic properties of two estimators for the partially linear single-index models

The outer product of gradients (OPG) estimation procedure based on least squares (LS) approach has been presented by Xia et al. [An adaptive estimation of dimension reduction space. J Roy Statist Soc Ser B. 2002;64:363–410] to estimate the single-index parameter in partially linear single-index models (PLSIM). However, its asymptotic property has not been established yet and the efficiency of LS-based method can be significantly affected by outliers and heavy-tailed distributions. In this paper, we firstly derive the asymptotic property of OPG estimator developed by Xia et al. [An adaptive estimation of dimension reduction space. J Roy Statist Soc Ser B. 2002;64:363–410] in theory, and a novel robust estimation procedure combining the ideas of OPG and local rank (LR) inference is further developed for PLSIM along with its theoretical property. Then, we theoretically derive the asymptotic relative efficiency (ARE) of the proposed LR-based procedure with respect to LS-based method, which is shown to possess an expression that is closely related to that of the signed-rank Wilcoxon test in comparison with the t-test. Moreover, we demonstrate that the new proposed estimator has a great efficiency gain across a wide spectrum of non-normal error distributions and almost not lose any efficiency for the normal error. Even in the worst case scenarios, the ARE owns a lower bound equalling to 0.864 for estimating the single-index parameter and a lower bound being 0.8896 for estimating the nonparametric function respectively, versus the LS-based estimators. Finally, some Monte Carlo simulations and a real data analysis are conducted to illustrate the finite sample performance of the estimators.     

Empirical Likelihood Inference for the Parameter in Additive Partially Linear EV Models

In this article, we consider empirical likelihood inference for the parameter in the additive partially linear models when the linear covariate is measured with error. By correcting for attenuation, a corrected-attenuation empirical log-likelihood ratio statistic for the unknown parameter β, which is of primary interest, is suggested. We show that the proposed statistic is asymptotically standard chi-square distribution without requiring the undersmoothing of the nonparametric components, and hence it can be directly used to construct the confidence region for the parameter β. Some simulations indicate that, in terms of comparison between coverage probabilities and average lengths of the confidence intervals, the proposed method performs better than the profile-based least-squares method. We also give the maximum empirical likelihood estimator (MELE) for the unknown parameter β, and prove the MELE is asymptotically normal under some mild conditions.     

Estimation Bias in Complete-Case Analysis in Crossover Studies with Missing Data

Crossover designs are used often in clinical trials. It is not uncommon that subjects discontinue before completing all treatment periods in a crossover study. Despite availability of statistical methodologies utilizing all available data and software for obtaining valid inferences under the assumption of missing at random (MAR), naïve approaches, such as the complete case (CC) analysis, which is only valid with a strong assumption of missing completely at random are still widely used in practice. In this article, we obtain the analytical form of the estimation bias of treatment effects with CC for linear mixed models. We use simulation studies to examine the inflation of Type I error and efficiency loss in the inferences with CC under MAR. Invalidity and inefficiency of two other commonly used approaches for defining analyzed data in the presence of missing data, including data from at least two periods in three period crossover and available cases for a specific comparison of interest, are also demonstrated through simulation studies.