我国房地产投资是否具有挤出效应?——基于I(2)VECM的分析

本文实证检验我国房地产与非房地产投资之间的关系,通过构建两者资本存量之间的二阶单整(I(2))向量自回归(VAR)模型,两者投资额之间的一阶单整(I(1))VAR模型,以及投资增长速度之间的平稳(I(0))VAR模型,运用协整分析技术,将两者之间的关系分为长期、中期、短期并进行了实证检验。本文还对房地产投资的挤出效应进行了检验。长期来看,房地产投资对非房地产投资具有明显的挤出效应,而非房地产投资能够促进房地产投资的增长。     

我国国债规模对宏观经济影响实证分析

随着以希腊债务危机为导火索的欧洲债务危机的愈演愈烈,国债对宏观经济的影响再一次成为了经济学讨论的热点.首先从直观图形着手,运用H-P滤度法去除趋势后,计算各变量的偏离趋势百分比,对比国债规模代替变量与宏观经济效应代替变量之间的偏离趋势图,从图形得到的定量关系为后面的实证分析做好准备.然后进一步进行协整分析,并在向量自回归(VAR)框架下通过脉冲响应函数考察变量之间的相互影响路径,最后通过建立误差修正模型(ECM)分析各个变量之间的长期均衡关系和短期波动特征,以量化各变量之间影响程度的大小.全面系统地研究了国债对宏观经济增长的影响程度并做出实证分析,对于深刻认识国债的本质,规避国债的风险,科学合理地制定国债政策有着重要的理论价值和实际意义.     

外汇储备增加对基础货币投放的影响——基于协整方法与VAR模型的实证检验

在理论上,由于外汇储备在货币当局的资产负债表中处于资产方,因而它的增加将导致负债或其他资产的波动,最终使得基础货币投放的增加。本文采用1994年1季度至2007年1季度的数据,运用协整方法、向量自回归(VAR)模型实证检验了中国外汇储备增加对货币投放的影响.实证检验结果显示了中国外汇储备增加对基础货币投放的影响程度,以及影响的滞后性、持久性与稳定性特征,从而为货币当局实行开放经济下的货币政策提供一定的实证依据。     

基于非均衡理论研究国际石油价格波动对中国经济的影响

国际石油价格波动对中国经济的影响越来越受到人们的关注。基于现代经济学非均衡理论,根据道格拉斯生产函数找出石油价格波动对中国经济的影响作用机制。通过研究发现石油价格与经济增长、石油生产资料价格之间存在一定关系;然后又运用约翰森"协整"检验方法检验变量之间可能存在的协整方程的个数;最后利用包含一个协整约束的向量误差修正模型(VECM)分析石油价格波动对中国经济的影响。研究结果表明:从长期来看,石油价格上涨对中国经济增长具有负面的效应,而从短期来看,石油价格上涨对中国经济的增长有一定的刺激作用。     

中国对外贸易的向量误差修正模型

对外贸易与经济增长关系一直是经济研究中的热点问题,在对外贸易取得了长足发展的今天这一问题显得更加重要。本文对相关的研究文献进行了评述,并采用四个变量系统的VAR模型和VECM模型,对我国对外贸易、产出和投资的关系进行了实证研究,实证结论证明了对外贸易对经济增长不管是长期还是短期都存在正的影响,并提出了改变对外贸易增长方式和鼓励进口的政策建议。     

基于VAR—GM(1.1)—SVR模型的房地产价格预测研究

为提高房地产价格预测精度,克服传统统计数据真实性低、时效性差的缺点,本文以网络搜索数据为基础,首先通过斯皮尔曼相关分析和时差相关分析筛选出与房地产价格具有高度相关性的先行关键词,并利用向量自回归模型(VAR)和GM(1.1)模型分别预测房地产价格;然后构建基于向量自回归模型和GM(1.1)模型的VAR—GM(1.1)—SVR模型将以上两个模型的预测结果进行预测融合,并以西安市数据为例进行验证,得出均方误差(MSE)和标准平均方差(NMSE)分别为0.97和0.03,优于单一模型预测效果.     

FDI与中国对外贸易的向量误差修正模型

本文以中国的经济数据对外国直接投资(FDI)与对外贸易的联系做了实证检验,根据协整理论建立向量误差修正模型对此问题予以分析,得出结论是在长期和短期内进入中国的外国直接投资与中国的出口都是互补联系,同时在短期外国直接投资与中国的进口也是互补,而在长期外国直接投资与中国的进口却是替代联系。     

基于VAR和DAG的中国经济增长实证研究

本文利用近年来新发展的DAG方法对我国的GDP、投资、消费和出口的因果关系进行研究.与以前的研究方法相比较,DAG方法可为宏观经济变量的结构VAR模型的过度识别提供限制,同时能给出经济变量的同期和动态因果关系.实证研究表明,投资和消费既是我国GDP增长的同期原因,又是经济增长的短期和长期原因,而且实证结论不支持我国的出口导向型经济增长假设.     

山东省CO_2排放与城镇化、国际化之间关系研究

城镇化、国际化发展带来环境问题引起人们关注.基于此,利用山东省1995-2012年的样本数据,运用自回归分布滞后(ARDL)模型研究城镇化、外商直接投资与CO_2排放量之间长、短期关系.研究表明,人均CO_2排放量与人均外商直接投资(FDI)、城镇化以及人均能源消费量之间存在协整关系.长期看,城镇化进程造成了对环境污染;外商直接投资并未造成对环境破坏,反而在一定程度上促进了山东省产业结构调整;能源消费量与CO_2排放量呈正比.     

人民币汇率波动与我国房价关系的实证分析

分析了汇率影响房价的机制,利用人民币实际有效汇率、房地产价格指数和银行同业拆借利率共30组数据(2005,07～2007,12)建立了向量自回归模型(VAR),并使用协整、Granger因果检验,脉冲响应分析对人民币汇率波动与我国房地产价格之间的关系进行实证检验.研究结果表明,人民币实际有效汇率对房地产价格产生正向影响,人民币升值是引起房价上涨的格兰杰原因.在现阶段,控制因人民币升值而进入中国的境外资金过度流入房地产市场,有利于保持我国房地产价格稳定.     

石油价格波动与我国进口价格的动态关系研究——基于对油价冲击分解的结构VAR模型

以2000～2008年我国月度数据为研究样本,从油价冲击的正负冲击角度分析国际石油价格波动与我国进口价格之间的存在的动态传导关系,并对其产生的原因进行了讨论.首先采用结构VAR(SVAR)模型对我国进口价格受到的油价冲击进行结构分解,其次用移动平均形式的SVAR模型分析不同油价冲击对进口价格的影响.结果表明,油价上涨对进口价格有显著的正向拉动作用,同期经济需求上涨推动进口价格有较大涨幅;而油价下跌时进口价格跌幅较小,甚至与油价波动存在负相关关系,这主要是由经济需求增加造成的.因此,研究石油价格对进口价格水平的动态传导关系.要区分油价冲击形式,这一结论有助于政府部门针对油价正负冲击的不同影响来制定应对政策,确保经济的稳定发展.     

结构VAR的有向非循环图模型

研究用图模型方法辨识结构向量自回归(VAR)模型,图中的结点表示不同时刻的随机变量,结点间的边表示其所表示的随机变量之间存在的因果相依关系.针对建立有向非循环图的问题,提出了一种基于回归分析的判断方法,用回归方程的回归平方和之差作为统计量,确定当前变量之间相依关系的方向.与R ea le的逐一判别法和A lessio的图搜索方法相比,文中提出的基于统计分析的方法简单易行,且可获得唯一的当前变量有向非循环图.最后以两组模拟序列为例,验证了所提出的方法是可行且有效的.     

利用计数统计模型方法评价大连星海公园旅游的经济价值

在非市场条件下如何评价旅游资源的经济价值是自然旅游公园管理工作中所面临的一个重要问题.正确地估计这类公园的旅游需求函数则是对其有效地进行经济价值评价的关键.由于旅游次数数据具有非负整数和截断的性质,使用传统的多元线性回归模型来估计旅游需求函数则会产生较大的偏差.因此。本文采用非线性最小二乘、截断泊松分布和截断负二项分布等计量经济学模型估计大连星海公园的旅游需求函数,以此来计算公园旅游的经济价值。     

Zero weights and non-zero slacks: Different solutions to the same problem

This paper re-assesses three independently developed approaches that are aimed at solving the problem of zero-weights or non-zero slacks in Data Envelopment Analysis (DEA). The methods are weights restricted, non-radial and extended facet DEA models. Weights restricted DEA models are dual to envelopment DEA models with restrictions on the dual variables (DEA weights) aimed at avoiding zero values for those weights; non-radial DEA models are envelopment models which avoid non-zero slacks in the input-output constraints. Finally, extended facet DEA models recognize that only projections on facets of full dimension correspond to well defined rates of substitution/transformation between all inputs/outputs which in turn correspond to non-zero weights in the multiplier version of the DEA model. We demonstrate how these methods are equivalent, not only in their aim but also in the solutions they yield. In addition, we show that the aforementioned methods modify the production frontier by extending existing facets or creating unobserved facets. Further we propose a new approach that uses weight restrictions to extend existing facets. This approach has some advantages in computational terms, because extended facet models normally make use of mixed integer programming models, which are computationally demanding.     

The interval efficiency based on the optimistic and pessimistic points of view

Data envelopment analysis (DEA) is a data-oriented approach for evaluating the performances of a set of peer entities called decision-making units (DMUs), whose performance is determined based on multiple measures. The traditional DEA, which is based on the concept of efficiency frontier (output frontier), determines the best efficiency score that can be assigned to each DMU. Based on these scores, DMUs are classified into DEA-efficient (optimistic efficient) or DEA-non-efficient (optimistic non-efficient) units, and the DEA-efficient DMUs determine the efficiency frontier. There is a comparable approach which uses the concept of inefficiency frontier (input frontier) for determining the worst relative efficiency score that can be assigned to each DMU. DMUs on the inefficiency frontier are specified as DEA-inefficient or pessimistic inefficient, and those that do not lie on the inefficient frontier, are declared to be DEA-non-inefficient or pessimistic non-inefficient. In this paper, we argue that both relative efficiencies should be considered simultaneously, and any approach that considers only one of them will be biased. For measuring the overall performance of the DMUs, we propose to integrate both efficiencies in the form of an interval, and we call the proposed DEA models for efficiency measurement the bounded DEA models. In this way, the efficiency interval provides the decision maker with all the possible values of efficiency, which reflect various perspectives. A numerical example is presented to illustrate the application of the proposed DEA models.     

A bi‐level neural‐based fuzzy classification approach for credit scoring problems

The credit scoring is a risk evaluation task considered as a critical decision for financial institutions in order to avoid wrong decision that may result in huge amount of losses. Classification models are one of the most widely used groups of data mining approaches that greatly help decision makers and managers to reduce their credit risk of granting credits to customers instead of intuitive experience or portfolio management. Accuracy is one of the most important criteria in order to choose a credit‐scoring model; and hence, the researches directed at improving upon the effectiveness of credit scoring models have never been stopped. In this article, a hybrid binary classification model, namely FMLP, is proposed for credit scoring, based on the basic concepts of fuzzy logic and artificial neural networks (ANNs). In the proposed model, instead of crisp weights and biases, used in traditional multilayer perceptrons (MLPs), fuzzy numbers are used in order to better model of the uncertainties and complexities in financial data sets. Empirical results of three well‐known benchmark credit data sets indicate that hybrid proposed model outperforms its component and also other those classification models such as support vector machines (SVMs), K‐nearest neighbor (KNN), quadratic discriminant analysis (QDA), and linear discriminant analysis (LDA). Therefore, it can be concluded that the proposed model can be an appropriate alternative tool for financial binary classification problems, especially in high uncertainty conditions. © 2013 Wiley Periodicals, Inc. Complexity 18: 46–57, 2013     

Modern analysis of customer satisfaction surveys: comparison of models and integrated analysis

Customer satisfaction is a key dimension driving business outcomes and performance of processes in service and product organizations. Measuring customer satisfaction is typically based on self‐declared or interview‐based questionnaires where users or consumers are asked to express opinions on statements, or satisfaction scales, mapping out various interactions with the service provider or product supplier. The topic has gained importance in recent years with researchers proposing new models and methods for designing, implementing, and analyzing customer satisfaction surveys. This paper builds on material presented in a recent edited book entitled Modern Analysis of Customer Satisfaction Surveys (Kenett and Salini, 2011). The book provides a comprehensive exposition of a variety of models that have all been applied to the same data set by leading experts. These models generate a variety of management insights. Combining models opens up opportunities for further research and applications. Specifically, we suggest that an integrated analysis, aggregating several approaches to survey data analysis, may prove effective in increasing the information quality derived from of a customer satisfaction survey. Copyright © 2011 John Wiley & Sons, Ltd.     

数据包络分析SBM超效率模型无可行解问题的两阶段求解法

数据包络分析是一种得到广泛应用的基于线性规划的非参数技术效率分析方法,其超效率模型是将被评价DMU从参照集中排除从而使求解得出的效率值可能大于1.超效率模型在文献中用于对有效的DMU进行排序、探测异常值、敏感性和稳定性分析、分析生产率变化的Malmquist模型、二人博弈模型等.超效率模型存在的一个缺陷是在规模收益可变的假设下会出现无可行解的问题.提出了一种基于两阶段求解的SBM超效率模型,并保持了与传统SBM超效率模型的兼容性:在传统的投入(产出)导向SBM超效率模型有可行解时,两阶段法获得的结果与之相同;在传统的投入(产出)导向SBM超效率模型无可行解时,两阶段超效率模型可以得出最接近投入(产出)导向定义的可行解.算例采用实际数据对方法进行了验证.     

Mixture models in econometric duration analysis

Econometric duration analysis has become an important part of methodology in econometrics, bringing forth a plenty of applications. The probability distribution of the duration of a time span is modelled through its conditional hazard rate given the covariates. When some of the covariates are unobservable, the duration, given the observable covariates, has a mixture distribution. The paper surveys and discusses recent developments in the specification, estimation, diagnosis and economic application of proportional hazard models with unobservables. Copyright © 2003 John Wiley & Sons, Ltd.