我国外资利用的经济增长效应——基于内生经济增长模型的分析

The flowing of foreign capital is related with the economic security of one countries. The experiencesand lessons of many developing countries show that the impact of the foreign capital to the domesticeconomic is often the most important factors of the financial crisis. So it is important to a analysis theforeign capital economic growth effect. Based on the endogenous economic growth model, this paper firstdistricts the three cases of the foreign capital impact and give the three factors of the foreign capitalaffecting to the economic path. The using the sample data of China, we analyze the three factors. Theresults show that all the three factor effets are all positive, which means that the foreign capital utility ofChina is efficiency.     

对外贸易与国内收入差距：基于全球125个国家的实证分析

 本文利用全球125个国家的统计数据,分所有国家、发达国家、发展中国家、亚洲发展中国家四个层面,实证分析了进出口贸易对世界各国以及不同类型国家国内收入差距的影响程度、作用以及在影响收入差距所有因素中的地位。研究结果表明：（1）对于所有国家来说,进口和出口对国家内部的收入差距影响系数较小且不显著,金融发展程度因素和高等教育因素是影响国家内部收入差距的重要因素。（2）对于发达国家来说,进口和出口因素是影响国家内部收入差距的主要因素,进口因素和出口因素的影响系数比较大且都比较显著,进口增加有利于扩大收入差距,出口增加有利于缩小收入差距。经济自由度也是影响发达国家国内收入差距的主要因素。（3）对于所有发展中国家来说,进口和出口因素对国家内部收入差距的影响系数较小且都不显著,只有外商直接投资因素和基础教育因素具有显著性。（4）对于亚洲发展中国家来说,进出口贸易因素是影响亚洲发展中国家国内收入差距的重要因素,进口有利于缩小收入差距,出口扩大了收入差距,与所有发展中国家的考察结果相比,进出口贸易对亚洲发展中国家国内收入差距的影响程度更大,且更加显著。     

The difficult and ubiquitous problems of multiplicities

Multiplicities are ubiquitous. They threaten every inference in every aspect of life. Despite the focus in statistics on multiplicities, statisticians underestimate their importance. One reason is that the focus is on methodology for known multiplicities. Silent multiplicities are much more important and they are insidious. Both frequentists and Bayesians have important contributions to make regarding problems of multiplicities. But neither group has an inside track. Frequentists and Bayesians working together is a promising way of making inroads into this knotty set of problems. Two experiments with identical results may well lead to very different statistical conclusions. So we will never be able to use a software package with default settings to resolve all problems of multiplicities. Every problem has unique aspects. And all problems require understanding the substantive area of application.     

中国银行信贷、房地产价格与宏观经济互动关系研究——基于VAR模型的实证分析

银行信贷在近几年有了飞速发展,但是有相当一部分资金流向了房地产业,而房地产业是国计民生的重要产业之一,在推动宏观经济增长方面起到了重要作用。在建立VAR模型的基础上综合运用协整检验、Granger因果关系检验等方法,对中国银行信贷、房地产价格与宏观经济间的互动关系进行研究,结果表明银行信贷、房地产价格与宏观经济三者之间确实存在互动关系。     

Generalized Method of Moments and Empirical Likelihood

Generalized method of moments (GMM) estimation has become an important unifying framework for inference in econometrics in the last 20 years. It can be thought of as encompassing almost all of the common estimation methods, such as maximum likelihood, ordinary least squares, instrumental variables, and two-stage least squares, and nowadays is an important part of all advanced econometrics textbooks. The GMM approach links nicely to economic theory where orthogonality conditions that can serve as such moment functions often arise from optimizing behavior of agents. Much work has been done on these methods since the seminal article by Hansen, and much remains in progress. This article discusses some of the developments since Hansen's original work. In particular, it focuses on some of the recent work on empirical likelihood–type estimators, which circumvent the need for a first step in which the optimal weight matrix is estimated and have attractive information theoretic interpretations.     

我国通货膨胀预期的微观基础研究

 通货膨胀预期的微观基础是一个重要的理论问题和实践问题,但是至今为止相关的研究还非常少。本文采用中国人民银行的《居民储蓄问卷调查系统》数据库,从经济主体的经济特征和人口统计特征两方面详细研究哪些因素会显著影响居民通胀预期。研究结果表明：人们对未来家庭经济状况的信心、风险厌恶程度和金融参与及熟悉度、家庭所处阶层、人们对现阶段经济运行状况的满意程度以及最近家庭所处的经济环境都是形成异质性通胀预期的重要原因。研究结果对于制定更加科学的货币政策和加强通胀预期管理具有重要的参考价值。     

Non-concave penalization in linear mixed-effect models and regularized selection of fixed effects

Mixed-effect models are very popular for analyzing data with a hierarchical structure. In medical applications, typical examples include repeated observations within subjects in a longitudinal design, patients nested within centers in a multicenter design. However, recently, due to the medical advances, the number of fixed-effect covariates collected from each patient can be quite large, e.g., data on gene expressions of each patient, and all of these variables are not necessarily important for the outcome. So, it is very important to choose the relevant covariates correctly for obtaining the optimal inference for the overall study. On the other hand, the relevant random effects will often be low-dimensional and pre-specified. In this paper, we consider regularized selection of important fixed-effect variables in linear mixed-effect models along with maximum penalized likelihood estimation of both fixed and random-effect parameters based on general non-concave penalties. Asymptotic and variable selection consistency with oracle properties are proved for low-dimensional cases as well as for high dimensionality of non-polynomial order of sample size (number of parameters is much larger than sample size). We also provide a suitable computationally efficient algorithm for implementation. Additionally, all the theoretical results are proved for a general non-convex optimization problem that applies to several important situations well beyond the mixed model setup (like finite mixture of regressions) illustrating the huge range of applicability of our proposal.     

Evaluating the Calibration of Multi-Step-Ahead Density Forecasts Using Raw Moments

The evaluation of multi-step-ahead density forecasts is complicated by the serial correlation of the corresponding probability integral transforms. In the literature, three testing approaches can be found that take this problem into account. However, these approaches rely on data-dependent critical values, ignore important information and, therefore lack power, or suffer from size distortions even asymptotically. This article proposes a new testing approach based on raw moments. It is extremely easy to implement, uses standard critical values, can include all moments regarded as important, and has correct asymptotic size. It is found to have good size and power properties in finite samples if it is based on the (standardized) probability integral transforms.     

Bayesian significance test for discriminating between survival distributions

An evaluation of FBST, Fully Bayesian Significance Test, restricted to survival models is the main objective of the present paper. A Survival distribution should be chosen among the tree celebrated ones, lognormal, gamma, and Weibull. For this discrimination, a linear mixture of the three distributions is an important tool: the FBST is used to test the hypotheses defined on the mixture weights space. Another feature of the paper is that all three distributions are reparametrized in that all the six parameters are written as functions of the mean and the variance of the population been studied. Some numerical results from simulations with some right-censored data are considered.     

A sequential design for binomial clinical trials

We investigate a sequential procedure for comparing two treatments in a binomial clinical trial. The procedure uses play-the-winner sampling with termination as soon as the absolute difference in the number of successes of the two treatments reaches a critical value. The important aspect of our procedure is that the critical value is modified as the experiment progresses. Numerical results are given which show that this procedure is preferred to all other existing procedures on the basis of the sample size on the poorer treatment and also on the basis of total sample size.     

Heteroscedastic Global Tests that the Regression Parameters for Two or More Independent Groups are Identical

Simulation results are reported on methods that allow both within group and between group heteroscedasticity when testing the hypothesis that independent groups have identical regression parameters. The methods are based on a combination of extant techniques, but their finite-sample properties have not been studied. Included are results on the impact of removing all leverage points or just bad leverage points. The method used to identify leverage points can be important and can improve control over the Type I error probability. Results are illustrated using data from the Well Elderly II study.     

房地产价格区域间联动与泡沫的空间扩散 ——基于2000－2005年中国35个大中城市面板数据的实证检验

 利用中国35个大中城市2000－2005年间的面板数据，本文首先对房地产价格中的泡沫成分进行测度；然后构建泡沫自回归模型，并对其残差进行CSD检验，结果显示不同城市间房地产泡沫的演化过程相互影响。与前人研究不同，以房地产泡沫为研究对象，不仅可以确认中国不同城市间房地产价格存在联动性，而且可以证明消费者的适应性预期是其中重要的传导机制之一。     

我国经济增长影响因素和当前形势分析

本文通过对影响我国经济增长的因素进行了定量分析，以论证我国目前在全球经济危机下所采取的宏观经济政策。社会总需求是影响经济的主要因素，消费和投资是国内需求，出口是国外需求，这三个经济变量都以不同程度地影响经济增长。本文通过数据定量分析了消费（CU）、投资（1）、出口（EX）三个变量对我国GDP的贡献，同时分析了我国目前的经济形势，从而论证我国目前的宏观经济政策并提出建议。     

教育活动核算方法的统计研究

教育活动核算方法的统计研究李金华ＡＢＳＴＲＡＣＴＥｄｕｃａｔｉｏｎｉｓａｎｉｍｐｏｒｔａｎｔａｃｔｉｖｉｔｙｓｅｃｔｏｒｏｆｎａｔｉｏｎａｌｅｃｏｎｏｍｙ,ｉｔｓｄｅｖｅｌｏｐｍｅｎｔｓｔａｔｕｓｉｓａｍａｉｎｍａｒｋｏｆｓｏｃｉａｌｄｅｖｅｌｏｐｍ...     

高校青年教师压力、职业满意度与生活满意感的实证研究

采用结构方程模型,对江苏6所高校的309位青年教师的调查研究发现,生活压力对生活满意感、工作压力对职业现状满意度、发展压力对职业发展满意度的负向影响均显著。职业发展满意度对生活满意感正向影响显著,对发展压力与生活满意感关系的中介效应也显著。而职业现状满意度却不影响生活满意感,对工作压力与生活满意感关系的中介效应也不显著。因此,缓解发展压力,提升职业发展满意度至关重要。     

Identification of the important parameters in thermal building simulation models

Two parameter screening techniques, a sequential bifurcation technique and a factorial sampling method, have been applied to a building thermal model, used to predict thermal comfort performance of a building in its design stage. Combined application of both screening methods revealed a set of 12 important model parameters out of a total of 81, explaining 94% of the variability in the model output. These important parameters were identified by the factorial sampling method on the basis of 246 model evaluations, while sequential bifurcation only needed 52 evaluations. However, the factorial sampling scheme was effective in identifying of not only the important parameters, but also the directions of parameter main effects and the severity of interaction effects. This additional information showed that isolated application of the sequential bifurcation method would have been unreliable, as satisfaction of the inherent assumptions could not be guaranteed. Only on the basis of proper knowledge of the sign of the parameter main effects, adequate clustering of important parameters and transformation of the model output, all obtained from the results of the factorial sampling scheme, reliable and economic application of sequential bifurcation was possible.     

注意性告知与元认知策略对培训效果的影响研究

计算机审计培训是培养高素质审计人才的有效途径。注意性告知和元认知策略是影响计算机审计培训效果的重要因素。实证研究了注意性告知与元认知策略对培训效果的影响,研究发现:注意性告知与元认知策略都会对培训效果产生正向影响;注意性告知还会对元认知策略产生正向影响。此外,注意性告知通过元认知策略部分中介作用于培训效果。     

Exhaustive k-nearest-neighbour subspace clustering

Cluster analysis is an important technique of explorative data mining. It refers to a collection of statistical methods for learning the structure of data by solely exploring pairwise distances or similarities. Often meaningful structures are not detectable in these high-dimensional feature spaces. Relevant features can be obfuscated by noise from irrelevant measurements. These observations led to the design of subspace clustering algorithms, which can identify clusters that originate from different subsets of features. Hunting for clusters in arbitrary subspaces is intractable due to the infinite search space spanned by all feature combinations. In this work, we present a subspace clustering algorithm that can be applied for exhaustively screening all feature combinations of small- or medium-sized datasets (approximately 30 features). Based on a robustness analysis via subsampling we are able to identify a set of stable candidate subspace cluster solutions.     

Bayesian variable selection for multioutcome models through shared shrinkage

Variable selection over a potentially large set of covariates in a linear model is quite popular. In the Bayesian context, common prior choices can lead to a posterior expectation of the regression coefficients that is a sparse (or nearly sparse) vector with a few nonzero components, those covariates that are most important. This article extends the “global‐local” shrinkage idea to a scenario where one wishes to model multiple response variables simultaneously. Here, we have developed a variable selection method for a K‐outcome model (multivariate regression) that identifies the most important covariates across all outcomes. The prior for all regression coefficients is a mean zero normal with coefficient‐specific variance term that consists of a predictor‐specific factor (shared local shrinkage parameter) and a model‐specific factor (global shrinkage term) that differs in each model. The performance of our modeling approach is evaluated through simulation studies and a data example.     

Subgroup analysis and interpretation for phase 3 confirmatory trials: White paper of the EFSPI/PSI working group on subgroup analysis

Subgroup by treatment interaction assessments are routinely performed when analysing clinical trials and are particularly important for phase 3 trials where the results may affect regulatory labelling. Interpretation of such interactions is particularly difficult, as on one hand the subgroup finding can be due to chance, but equally such analyses are known to have a low chance of detecting differential treatment effects across subgroup levels, so may overlook important differences in therapeutic efficacy. EMA have therefore issued draft guidance on the use of subgroup analyses in this setting. Although this guidance provided clear proposals on the importance of pre‐specification of likely subgroup effects and how to use this when interpreting trial results, it is less clear which analysis methods would be reasonable, and how to interpret apparent subgroup effects in terms of whether further evaluation or action is necessary. A PSI/EFSPI Working Group has therefore been investigating a focused set of analysis approaches to assess treatment effect heterogeneity across subgroups in confirmatory clinical trials that take account of the number of subgroups explored and also investigating the ability of each method to detect such subgroup heterogeneity. This evaluation has shown that the plotting of standardised effects, bias‐adjusted bootstrapping method and SIDES method all perform more favourably than traditional approaches such as investigating all subgroup‐by‐treatment interactions individually or applying a global test of interaction. Therefore, these approaches should be considered to aid interpretation and provide context for observed results from subgroup analyses conducted for phase 3 clinical trials.