On comparing,classifying and clustering several dependent regression models

Classifying several regression models fitted on a dataset is one of the most problems in data analysis. In other words, scientists are interested in comparing several regression models that can be used for a dataset. In this paper, an approach will be used to compare and classify several dependent regression models. Then the performance of the proposed method is investigated using simulation study and real example.     

A misspecification test for the higher order co-moments of the factor model

The traditional estimation of higher order co-moments of non-normal random variables by the sample analog of the expectation faces a curse of dimensionality, as the number of parameters increases steeply when the dimension increases. Imposing a factor structure on the process solves this problem; however, it leads to the challenging task of selecting an appropriate factor model. This paper contributes by proposing a test that exploits the following feature: when the factor model is correctly specified, the higher order co-moments of the unexplained return variation are sparse. It recommends a general to specific approach for selecting the factor model by choosing the most parsimonious specification for which the sparsity assumption is satisfied. This approach uses a Wald or Gumbel test statistic for testing the joint statistical significance of the co-moments that are zero when the factor model is correctly specified. The asymptotic distribution of the test is derived. An extensive simulation study confirms the good finite sample properties of the approach. This paper illustrates the practical usefulness of factor selection on daily returns of random subsets of S&P 100 constituents.     

Diagnostics of multiple group influential observations for logistic regression models

In this paper, two new multiple influential observation detection methods, GCD.GSPR and mCD*, are introduced for logistic regression. The proposed diagnostic measures are compared with the generalized difference in fits (GDFFITS) and the generalized squared difference in beta (GSDFBETA), which are multiple influential diagnostics. The simulation study is conducted with one, two and five independent variable logistic regression models. The performance of the diagnostic measures is examined for a single contaminated independent variable for each model and in the case where all the independent variables are contaminated with certain contamination rates and intensity. In addition, the performance of the diagnostic measures is compared in terms of the correct identification rate and swamping rate via a frequently referred to data set in the literature.     

On the power of goodness-of-fit tests for the exponential distribution under progressive Type-II censoring

The aim of this article is to review existing goodness-of-fit tests for the exponential distribution under progressive Type-II censoring and to provide some new ideas and adjustments. In particular, we consider two-parameter exponentially distributed random variables and adapt the proposed test procedures to our scenario if necessary. Then, we compare their power by an extensive simulation study. Furthermore, we propose five new test procedures that provide reasonable alternatives to those already known.     

On Hotelling’s T2 test in a special paired sample case

In a special paired sample case, Hotelling’s T² test based on the differences of the paired random vectors is the likelihood ratio test for testing the hypothesis that the paired random vectors have the same mean; with respect to a special group of affine linear transformations it is the uniformly most powerful invariant test for the general alternative of a difference in mean. We present an elementary straightforward proof of this result. The likelihood ratio test for testing the hypothesis that the covariance structure is of the assumed special form is derived and discussed. Applications to real data are given.     

Inferences for a Partially Varying Coefficient Model With Endogenous Regressors

In this article, we propose a new class of semiparametric instrumental variable models with partially varying coefficients, in which the structural function has a partially linear form and the impact of endogenous structural variables can vary over different levels of some exogenous variables. We propose a three-step estimation procedure to estimate both functional and constant coefficients. The consistency and asymptotic normality of these proposed estimators are established. Moreover, a generalized F-test is developed to test whether the functional coefficients are of particular parametric forms with some underlying economic intuitions, and furthermore, the limiting distribution of the proposed generalized F-test statistic under the null hypothesis is established. Finally, we illustrate the finite sample performance of our approach with simulations and two real data examples in economics.     

地方高校重点学科建设绩效评价指标体系的验证性检验

对地方高校重点学科建设绩效评价指标体系进行验证性研究,检验其信效度和结构性.结果表明:指标体系咨询专家的积极系数、合理结构系数、权威系数都较高,肯德尔w系数为0.893,保证了研究较好的信效度;经聚类分析和非参数检验验证,各指标均能独立反映评价的不同信息,体现了较好的结构性.     

中国蔬菜价格波动与通货膨胀——基于波动来源的分解

利用Census X12季节调整和H-P滤波法,将蔬菜价格波动来源分解为趋势变动、季节变动、循环变动和不规则变动要素。利用Bootstrap因果检验与VAR模型,考察了蔬菜价格波动来源的分解因素与中国通货膨胀的关联性。结果表明,蔬菜价格波动影响消费者物价指数的主要渠道是通过季节变动和不规则变动要素;季节变动因素对消费者物价指数的影响呈季节周期性;不规则变动对消费者物价指数的冲击在最初时最显著,随后逐渐减弱。政策含义为,降低公众的通胀预期、促进蔬菜跨区域流通、控制蔬菜运输的物流成本、健全政府灾害天气应急响应机制均有利于减缓CPI上涨。     

以现代服务业为视角:“营改增”全面试行后存在的问题探析及建议

减税是当前世界税制改革的主线,随着营业税改征增值税工作的展开,我国结构性减税政策也逐步发挥了对我国现代服务业发展的积极作用。本文通过密切跟踪营改增自2013年8月1日全面试行后的运行情况,研究分析营业税改增值税后续改革进程中推行政策的难点,找出存在问题的症结,提出符合实际、切实可行的解决方案,为进一步深化财税改革提出建议。     

内部控制与审计相关问题的研究

现代审计倚重内部控制及控制测试.新审计准则重新定义了内部控制及控制测试的概念,修正了控制测试的情形及具体要求,适应了现代风险导向审计模式下对内部控制实施风险评估及控制测试的需要.正确理解内部控制与审计的关系,恰当界定内部控制及控制测试的涵义,对审计理论和实务均具有现实意义.