外国直接投资对于福建经济增长的影响分析

外国直接投资在福建经济增长过程中起到了非常重要的作用。文章运用因果检验和时间序列的单整、协整检验分析了外国直接投资和福建经济增长两者之间的关系,得出了外国直接投资和福建经济增长之间存在着长期稳定的互动关系的结论。     

中国电力消费与经济增长关系的实证研究

文章采用自回归分布滞后(ARDL)模型的边限协整检验和基于误差修正模型的格兰杰因果检验方法对我国1980～2009年之间的电力消费与经济增长进行了因果关系研究.边限协整检验比传统的协整检验更具稳健性,适合小样本分析.在实证分析中,首先将电力消费与GDP时间序列进行HP滤波分解为趋势部分和波动部分,分别进行协整检验和因果关系研究.分析结果表明,存在从电力消费到经济发展的显著单向长期因果关系,两者的波动之间存在明显的双向短期因果关系.此结论进一步说明了电力在我国经济社会发展中的重要地位.因此,必须加快我国电力建设来保证我国经济增长.     

"二元经济"的瓶颈与和谐社会战略的协整检验

在当前"二元经济"城乡分割的情况下,城乡居民收入存在较大差异.和谐社会战略要求经济的均衡发展,对城乡居民收入差的时间序列考察,是检验三农政策是否符合和谐社会建设的有戏方法之一.文章以甘肃省城镇居民人均可支配收入和农村居民人均纯收入为对象进行考查,利用单位根检验方法分析了改革开放以来二者时间序列的单整特征,并用协整理论检验了它们之间的长期均衡关系和收敛趋势.     

股市收益与通货膨胀率:中国数据的ARDL边界检验分析

本文基于Fisher理论假说,使用1997-2006年月度数据对中国股市收益与通货膨胀率之间的理论关系进行实证分析。运用ARDL边界检验法和Granger因果关系检验变量之间可能存在的长期和短期关系以及这些作用的方向。     

浙江民营企业外贸对经济增长的作用机理检验

民营企业是浙江经济的亮点,其对外贸易是浙江经济发展的主要推动力,因此研究民营企业外贸对浙江经济的作用机理很有理论价值和实践意义。文章利用ADF检验对变量平稳性进行考察,应用协整方法和LM检验法对变量的长期均衡进行分析,应用Granger因果检验法对变量的因果关系进行研究,应用误差修正模型得到变量之间的长期稳定关系。文章最终提出了相应的对策和建议。     

ADF单位根检验中联合检验LM统计量研究

 本文研究了ADF单位根检验中参数联合约束的拉格朗日乘数检验。首先,本文构建了4个LM统计量并推导了它们的极限分布;然后,运用蒙特卡罗试验,模拟了有限样本容量常用检验水平下的临界值,拟合了临界值关于样本容量的响应面函数,并总结了LM统计量有限样本容量下的统计特性;比较分析了这4个LM统计量的检验功效及实际检验水平;最后,一个实例分析简要说明了这几个统计量在单位根检验中的应用。     

华东地区能源价格与碳强度的协整分析

基于面板数据协整检验理论,利用中国华东地区省市2000～2008年间的数据资料,检验能源价格与碳强度等变量序列的平稳性及协整关系,并建立回归模型。结果表明:经济发达的省域,碳强度相对较低;能源价格与碳强度取对数后的序列均为一阶单整,存在长期均衡关系;Hausman检验认为能源价格、人口增长与碳强度间应建立个体固定效应回归模型;能源价格对碳强度负相关,提高能源价格能显著降低碳强度,但不同地区碳强度对能源价格变化的反应不同。     

能源消费与经济增长协整分析：基于宏观数据的比较分析

在综合比较分析关于中国GDP和能源消费的各种宏观统计数据的基础上,选定以Maddison估算的以1987年不变价格衡量的GDP和以IEA估算的能源消费为分析的基础数据,通过单位根检验、协整检验、格兰杰因果分析等分析技术发现：中国GDP和能源消费数据是一阶单整的,存在协整关系,并表现为从经济增长到能源消费的单向因果关系。而进一步的误差修正模型分析结果显示：经济增长和能源消费存在长期的均衡关系,GDP每增加1个百分点,能源消费将提高0．811个百分点;相反,能源消费增加一个百分点,GDP将提高0．194个百分点。这表明中国经济增长与能源消费之间存在密切的关系,能源消费并不是经济增长的一个强外生变量。     

含方程误差的重复测量误差模型参数的假设检验

含方程误差的重复测量误差模型解决了协变量真值与响应变量真值之间存在的不完全匹配问题.为使中小型样本量下的假设检验结果更为准确,文章基于多元正态分布推导改进形式的Skovgaard似然比检验统计量,提高其在原假设下收敛到卡方分布的渐近速度,并应用该检验统计量对重复测量误差模型中回归参数的显著性进行假设检验.模拟研究的结果表明改进的似然比检验统计量在有限样本检验下的优越性;实例分析中通过检验气温与气压之间回归参数的显著性来说明该方法的实用性.     

湖北省农村居民消费与收入关系的实证分析

一、湖北省农村居民消费与收入的协整分析 协整是指变量之间存在的长期均衡关系,可利用协整理论来检验农村居民的消费与收入之间是否存在这种长期稳定的均衡关系.     

How the one-way analysis of variance model is affected by the degree of precision of the data

The underlying theory upon which many statistical tests are based, assumes that the variables sampled are continuous. However, the data are often subject to rounding. Here we consider the effect of rounding on the significance level and power of the F test in the one-way analysis of variance with fixed effects, with respect to the degree of precision of recorded data, sample size and normality of the population. Even for coarse rounding the effects are small.     

Analysis of the cross-effects in a cross-lagged panel study

Panel studies are statistical studies in which two or more variables are observed for two or more subjects at two or more points in time. Cross-lagged panel studies are comprised of continuous variables which divide naturally into two sets, and otten the primary statistical issue Is to estimate and test the cross-effects which indicate the degree to which each set is related to the other over time. By taking a regression approach to modeling the relationships, we apply multivariate regression methodology to make inferences about the regression coefficients in a cross-lagged panel model. In particular we develop a test of the hypothesis that the regression coefficients indicating the cross-effects are equal and develop simultaneous confidence bounds for various linear combinations of these regression coefficients.     

Measurements of separation among probability densities or random variables

Distance between two probability densities or two random variables is a well established concept in statistics. The present paper considers generalizations of distances to separation measurements for three or more elements in a function space. Geometric intuition and examples from hypothesis testing suggest lower and upper bounds for such measurements in terms of pairwise distances; but also in L_p spaces some useful non-pairwise separation measurements always lie within these bounds. Examples of such separation measurements are the Bayes probability of correct classification among several arbitrary distributions, and the expected range among several random variables.     

Efficient designs for Bayesian networks with sub-tree bounds

We present upper and lower bounds for information measures, and use these to find the optimal design of experiments for Bayesian networks. The bounds are inspired by properties of the junction tree algorithm, which is commonly used for calculating conditional probabilities in graphical models like Bayesian networks. We demonstrate methods for iteratively improving the upper and lower bounds until they are sufficiently tight. We illustrate properties of the algorithm by tutorial examples in the case where we want to ensure optimality and for the case where the goal is an approximate solution with a guarantee. We further use the bounds to accelerate established algorithms for constructing useful designs. An example with petroleum fields in the North Sea is studied, where the design problem is related to exploration drilling campaigns. All of our examples consider binary random variables, but the theory can also be applied to other discrete or continuous distributions.     

Probabilistic existence theorems in group testing

For a wide range of combinatorial group testing problems including additive, binary and multiaccess channel models, a probabilistic method is developed to derive upper bounds for the length of optimal nonsequential designs. A general result is proven allowing in many particular cases to compute the asymptotic bounds. The existence theorems are also derived for the situation when several errors in the test results can occur (searching with lies) and for the group testing problem in the binomial sample.     

Simulated annealing for the bounds of Kendall's τ and Spearman's ρ

It has long been known that, for many joint distributions, Kendall's τ and Spearman's ρ have different values, as they measure different aspects of the dependence structure. Although the classical inequalities between Kendall's τ and Spearman's ρ for pairs of random variables are given, the joint distributions which can attain the bounds between Kendall's τ and Spearman's ρ are difficult to find. We use the simulated annealing method to find the bounds for ρ in terms of τ and its corresponding joint distribution which can attain those bounds. Furthermore, using this same method, we find the improved bounds between τ and ρ, which is different from that given by Durbin and Stuart.     

Controlled Direct and Mediated Effects: Definition,Identification and Bounds

Abstract. Results are given which provide bounds for controlled direct effects when nounmeasured confounding assumptions required for the identification of these effects do not hold. Previous results concerning bounds for controlled direct effects rely on monotonicity relationships between the treatment, mediator and the outcome themselves; the results presented in this article instead assume that monotonicity relationships hold between the unmeasured confounding variable or variables and the treatment, mediator and outcome. Whereas prior results give bounds that contain the null hypothesis of no direct effect, the results presented here will in many instances yield bounds that do not contain the null hypothesis of no direct effect. For contexts in which a set of variables intercepts all paths between a treatment and an outcome, it is possible to provide a definition for a controlled mediated effect. We discuss the identification of these controlled mediated effects; the bounds for controlled direct effects are applicable also to controlled mediated effects. An example is given to illustrate how the results in the article can be used to draw inferences about direct and mediated effects in the presence of unmeasured confounding variables.     

On power and necessary sample sizes of the wilcoxon-mann-whitney test

The Wilcoxon-Mann-Whitney test is a frequently applied nonparametric procedure for testing the equality of two distributions. In this paper formulae for the necessary sample sizes and lower bounds for the power are derived applicable to arbitrary distributions. In particular, the case of discrete random variables is investigated. It is shown how the power bounds increase and the necessary sample sizes decrease when the number of possible outcomes decreases.     

Confidence Bounds for Positive Ratios of Normal Random Variables

Some applications of ratios of normal random variables require both the numerator and denominator of the ratio to be positive if the ratio is to have a meaningful interpretation. In these applications, there may also be substantial likelihood that the variables will assume negative values. An example of such an application is when comparisons are made in which treatments may have either efficacious or deleterious effects on different trials. Classical theory on ratios of normal variables has focused on the distribution of the ratio and has not formally incorporated this practical consideration. When this issue has arisen, approximations have been used to address it. In this article, we provide an exact method for determining (1 ? α) confidence bounds for ratios of normal variables under the constraint that the ratio is composed of positive values and connect this theory to classical work in this area. We then illustrate several practical applications of this method.     

Moderate deviations for the random weighted sums of END random variables with consistently varying tails

In this paper, we study moderate deviations for random weighted sums of extended negative dependent (END) random variables, which are consistently-varying tailed and not necessarily identically distributed. When these END random variables are independent of their weights, and the weights are positive random variables with two-sided bounds, the results shows END structure and the dependence between the weights have no effects on the asymptotic behavior of moderate deviations of partial sums and random sums.