服务企业竞争力组合的评价模型构建

文章构建了包含企业资源、企业能力和企业绩效三方面28个指标的服务企业竞争力评价指标体系，建立了服务企业竞争力组合评价模型。对单一评价模型结果运用KENDALL协和系数法方法进行事前相容性检验，建立平均值、Borda、Copeland和模糊Borda组合评价模型，运用Spearsman等级相关系数方法对组合评价结论进行事后一致性检验。输出最佳评价结果。     

组合评价法研究

组合评价法是近年来发展起来的一类综合评价方法。在分析单一综合评价法的基础上,本文引入了组合评价法,详细阐述了目前国内文献中出现的各种组合方法,对组合评价法的优点和缺点作了总结,并提出了对组合评价法的几点看法。     

煤矿工人“统计”的生命价值研究

 目前国内主要使用人力资本理论计算生命价值，反映了其他人事后对死者生命价值的评价。事前预防和控制灾害时，需要考虑死者事前对自己生命价值的评价。本文应用享乐价格理论，根据对四个煤矿调查得到的5875个井下工人样本，从工资决定模型中分解出死亡风险减少与愿意支付工资的定量关系，计算出井下工人事前为了减少一人死亡愿意支付的金额，即“统计”的生命价值。“统计”的生命价值可以作为评价预防和控制灾害技术经济合理性的重要依据。     

EXCEL单键实现Borda法组合评价

组合评价是将几种评价结果进行组合的一种科学合理的评价方法,Borda法是一种对几种单一评价法的评价排序结果进行组合的有效方法,但在实践中还没有可用的计算机软件,因而操作起来相当繁琐。利用VBA(VisualBasicforApplication)在EXCEL下编写宏程序,单键即可轻松实现Borda法组合评价。     

基于Monte Carlo随机模拟的几种正态性检验方法的比较

文章概述了几种主要的正态性检验方法,指出了它们的联系和区别.在Monte Carlo随机模拟的基础上,计算了Shapiro-Wilk检验、Kolmogrov-Smirnov检验、Gramer-von Mises检验和Anderson-Darling检验等四种检验方法在显著性水平为0.01,0.05和0.1,样本容量为10,20,30和100的条件下的检验功效.并在比较和分析各检验方法功效的基础上,给出了相关结论和建议.     

政策评估指标体系的构建

1政策评估政策评估亦称政策评价,是政策运行过程的重要环节,也是政策运行科学化的重要保障。关于政策评估的概念至今学术界未能达成统一的共识,一般认为,广义的“政策评估”包括政策的事前评估、执行评估、事后评估三种类型。但目前国外一些学者将政策的事前评估归入“政策分析     

基于GARCH模型的上证综合指数VaR计算

文章针对用参数法计算VaR时所存在的局限性,探讨了用GARCH模型计算VaR的新方法.并以我国上证综合指数为例,说明新方法计算VaR的基本思路以及对计算出的每日VaR如何进行事后检验.     

残差块形自助法在非对称单位根检验中的适用性

非对称单位根检验已成为时间序列分析中重要研究领域之一。而当随机干扰项之间具有一般性的自相关时,非对称单位根检验式中,由于不同的滞后阶会对统计量检验势产生至关重要的影响,因此采用残差块形自助法（RBB）对非对称单位根EG检验进行有效的改进研究,并对RBB法的适用性进行了模拟。结果表明：RBB法不仅在一定程度上降低了检验水平扭曲,而且大大提高了EG法的检验势。     

基于集对分析的组合评价方法

目前国内外提出的综合评价方法有几十种之多,如综合指数法、层次分析法、熵值法、主成分分析法等.这些方法可统称为单一评价法,单一评价法可分为两大类:主观赋权评价法和客观赋权评价法.前者如Delphi法、综合指数法等,后者如熵值法,主成分分析法等.评价方法不同在于评价者出发的角度不同,为了让评价的结果让人信服,有专家提出有必要选择多种方法进行评价,再将几种评价方法结果进行组合,这就是所谓的组合评价方法.     

统计学中的组合思想初探

不管有没有意识到，人们都在大量、广泛地使用着组合思想，可以说，组合思想已经渗透到各个领域。本文试图对统计学中的组合思想加以总结，并在此基础上对组合思想作一理论上的分析。 一、统计学中的组合思想 １组合评价 在多指标综合评价中，人们提出了各种各样的评价方法。按赋权方法的不同，单一综合评价方法可分为两大类：主观赋权评价法和客观赋权评价法。然而，无论是选用主观赋权评价法，还是选用客观赋权评价法进行综合评价，都有其自身无法解决的缺陷。主观赋权法虽然能充分吸收本领域专家高深的理论知识和丰富经验，体现出各个指…     

异常值对计量建模影响的典型案例

在经济计量建模过程中,异常值的影响评价与诊断问题越来越显得重要。本文旨在提供异常值对复共线性关系检验、序列相关性检验、异方差性检验、单位根检验等经济计量检验产生致命影响的典型案例,为经济计量学的教学与相关建模理论研究提供有说服力的数据资料。     

A differencing test

This paper proposes and applies a test procedure for misspecification in a dynamic regression model with moving average errors. The test statistics are based on testing for unit roots in the moving average process when the model is deliberately overdifferenced.     

An introduction to econometric applications of empirical process theory for dependent random variables

This paper discusses some uses in econometrics of empirical process theory for dependent rendom variables. Examples considered include non-standard parametric hypotheses tests and semiparametric estimation. The application of bracketing functional limit results is discussed in some detail     

Nonparametric Simultaneous Tests for Location and Scale Testing: A Comparison of Several Methods

The two-sample location-scale problem arises in many situations like climate dynamics, bioinformatics, medicine, and finance. To address this problem, the nonparametric approach is considered because in practice, the normal assumption is often not fulfilled or the observations are too few to rely on the central limit theorem, and moreover outliers, heavy tails and skewness may be possible. In these situations, a nonparametric test is generally more robust and powerful than a parametric test. Various nonparametric tests have been proposed for the two-sample location-scale problem. In particular, we consider tests due to Lepage, Cucconi, Podgor-Gastwirth, Neuhäuser, Zhang, and Murakami. So far all these tests have not been compared. Moreover, for the Neuhäuser test and the Murakami test, the power has not been studied in detail. It is the aim of the article to review and compare these tests for the jointly detection of location and scale changes by means of a very detailed simulation study. It is shown that both the Podgor–Gastwirth test and the computationally simpler Cucconi test are preferable. Two actual examples within the medical context are discussed.     

Discreteness Causes Bias in Percentage-Based Comparisons: A Case Study From Educational Testing

Discretizing continuous distributions can lead to bias in parameter estimates. We present a case study from educational testing that illustrates dramatic consequences of discreteness when discretizing partitions differ across distributions. The percentage of test takers who score above a certain cutoff score (percent above cutoff, or “PAC”) often describes overall performance on a test. Year-over-year changes in PAC, or ΔPAC, have gained prominence under recent U.S. education policies, with public schools facing sanctions if they fail to meet PAC targets. In this article, we describe how test score distributions act as continuous distributions that are discretized inconsistently over time. We show that this can propagate considerable bias to PAC trends, where positive ΔPACs appear negative, and vice versa, for a substantial number of actual tests. A simple model shows that this bias applies to any comparison of PAC statistics in which values for one distribution are discretized differently from values for the other.     

IV估计框架下模型设定检验问题的讨论

 IV估计框架下各种统计量的良好性质依赖于相应的模型设定,如果这些模型设定未能得到数据的支持,其统计推断结论将是不可靠的。如判定计量经济模型是否存在内生性的Hausman检验,实证研究中同一问题的检验结果可能大相径庭。如何通过合理的模型设定检验程序来获得模型参数科学、可靠的估计结果和检验结论呢？本文讨论了工具变量估计框架下的各种模型设定检验问题,明确了各个检验统计量的适用条件及其逻辑联系,给出了工具变量估计框架下模型设定检验的一般步骤。     

How Important Is the Effect of Rounding in Goodness-of-Fit

Abstract

In the area of goodness-of-fit there is a clear distinction between the problem of testing the fit of a continuous distribution and that of testing a discrete distribution. In all continuous problems the data is recorded with a limited number of decimals, so in theory one could say that the problem is always of a discrete nature, but it is a common practice to ignore discretization and proceed as if the data is continuous. It is therefore an interesting question whether in a given problem of test of fit, the “limited resolution” in the observed recorded values may be or may be not of concern, if the analysis done ignores this implied discretization. In this article, we address the problem of testing the fit of a continuous distribution with data recorded with a limited resolution. A measure for the degree of discretization is proposed which involves the size of the rounding interval, the dispersion in the underlying distribution and the sample size. This measure is shown to be a key characteristic which allows comparison, in different problems, of the amount of discretization involved. Some asymptotic results are given for the distribution of the EDF (empirical distribution function) statistics that explicitly depend on the above mentioned measure of degree of discretization. The results obtained are illustrated with some simulations for testing normality when the parameters are known and also when they are unknown. The asymptotic distributions are shown to be an accurate approximation for the true finite n distribution obtained by Monte Carlo. A real example from image analysis is also discussed. The conclusion drawn is that in the cases where the value of the measure for the degree of discretization is not “large”, the practice of ignoring discreteness is of no concern. However, when this value is “large”, the effect of ignoring discreteness leads to an exceded number of rejections of the distribution tested, as compared to what would be the number of rejections if no rounding is taking into account. The error made in the number of rejections might be huge.     

Selecting Regressors for Prediction Using PRESS and White t Statistics

It is shown that selecting regressors based on the prediction error sum of squares is asymptotically related to hypothesis testing with White's (1980) heteroscedasticity-consistent covariance matrix. A simulation experiment suggests that this asymptotic relation may be useful. Illustrative examples are also given.     

Simple Tests for Exogeneity of a Binary Explanatory Variable in Count Data Regression Models

This article investigates power and size of some tests for exogeneity of a binary explanatory variable in count models by conducting extensive Monte Carlo simulations. The tests under consideration are Hausman contrast tests as well as univariate Wald tests, including a new test of notably easy implementation. Performance of the tests is explored under misspecification of the underlying model and under different conditions regarding the instruments. The results indicate that often the tests that are simpler to estimate outperform tests that are more demanding. This is especially the case for the new test.     

Testing Variability in the Two-Sample Case

A number of robust methods for testing variability have been reported in previous literature. An examination of these procedures for a wide variety of populations confirms their general robustness. Shoemaker's improvement of the F test extends that test use to a realistic variety of population shapes. However, a combination of the Brown–Forsythe and O'Brien methods based on testing kurtosis is shown to be conservative for a wide range of sample sizes and population distributions. The composite test is also shown to be more powerful in most conditions than other conservative procedures.