Partial unit root and surplus-lag Granger causality testing: A Monte Carlo simulation study

Previous literature has shown that the addition of an untested surplus-lag Granger causality test can provide highly robust to stationary, non stationary, long memory, and structural break processes in the forcing variables. This study extends this approach to the partial unit root framework by simulation. Results show good size and power. Therefore, the surplus-lag approach is also robust to partial unit root processes.     

The Effects of Detrending in Granger Causality Tests

Nonstationary time series are frequently detrended in empirical investigations by regressing the series on time or a function of time. The effects of the detrending on the tests for causal relationships in the sense of Granger are investigated using quarterly U.S. data. The causal relationships between nominal or real GNP and M1, inferred from the Granger–Sims tests, are shown to depend very much on, among other factors, whether or not series are detrended. Detrending tends to remove or weaken causal relationships, and conversely, failure to detrend tends to introduce or enhance causal relationships. The study suggests that we need a more robust test or a better definition of causality.     

Testing the Equality of Two Exponential Distributions

This paper proposes an overlapping-based test statistic for testing the equality of two exponential distributions with different scale and location parameters. The test statistic is defined as the maximum likelihood estimate of the Weitzman's overlapping coefficient, which estimates the agreement of two densities. The proposed test statistic is derived in closed form. Simulated critical points are generated for the proposed test statistic for various sample sizes and significance levels via Monte Carlo Simulations. Statistical powers of the proposed test are computed via simulation studies and compared to those of the existing Log likelihood ratio test.     

中国宏观经济对沪市的波动溢出效应研究——基于VAR（k）-MGARCH—BEKK（1，1）模型

本文基于VAR（k）-MGARCH—BEKK（1,1）模型和Wald检验探讨了2003—2008年期间的全国银行间同业拆借利率、汇率、企业商品价格指数、工业增加值增长率、M0、宏观景气指数和消费价格指数对沪市的波动溢出效应,结果表明,除了企业商品价格指数和M0之外,其它宏观经济指标的增长率（速度）均对沪市收益率产生显著的、直接的波动溢出效应。     

Pooling multivariate data under W, LR and LM tests

Two independent random samples are drawn from two multivariate normal populations with mean vectors μ1 and μ2 and a common variance-covariance matrix Σ. Ahmed and Saleh (1990) considered preliminary test maximum likelihood estimator (PMLTE) for estimating μ1 based on the Hotelling's T _N ², when it is suspected that μ1=μ2. In this paper, the PTMLE based on the Wald (W), Likelihood Ratio (LR) and Lagrangian Multiplier (LM) tests are considered. Using the quadratic risk function, the conditions of superiority of the proposed estimator for departure parameter are derived. A max-min rule for the size of the preliminary test of significance is presented. It is demonstrated that the PTMLE based on W test produces the highest minimum guaranteed efficiencies compared to UMLE among the three test procedures.     

试析短跑放松技术的理论方法及检测

文章从生理学的角度,阐述了放松技术对提高短跑成绩的作用,认为放松技术主要指肌 肉放松。肌肉放松可增大肌肉的收缩力量和收缩速度,提高速度耐力和肌肉、关节的灵活性及柔 韧性。同时,从心理学的角度分析了短跑运动员的情绪、意志、自信心和竞争力等心理品质对肌 肉放松的影响,结合训练实践,针对性的提出了短跑放松训练方法和检测手段。     

国际航运发展综合试验区先行先试政策

从分析国际航运发展综合试验区先行先试的内涵机制出发,研究其发展战略定位;从政府航运监管政策、航运税制改革以及航运高端服务培育等几个方面研究其先行先试的政策体系,并结合国际经验,具体分析了相关航运政策.     

“以支定收”还是“以收定支”:我国财政收支关系的实证研究

通过对我国财政收入与支出的时序变量建立协整与误差修正模型,实证研究我国的财政收支关系中到底是以支定收还是以收定支,确定这种收支之间的因果关系对采取财政政策的借鉴意义。研究将回答,我国财政收入是财政支出之间是单向格兰杰因还是双向格兰杰因,抑或互非因果关系,并在此基础上指出财政政策的作用方向,为真正建立符合的现代市场经济的财政观提供经验证据。     

决定论的含义与真假问题

古代已有因果决定论和神灵决定论,近代学者又以力学规律充实因果决定论而使之发展成严格定量的形式。从中可以看到决定论的基本含义:过去未来的一切皆有定数;其之形成根据则是因果关系和客观规律的普遍存在。但自由意志论却长期支持着非决定论的存在;现代科学又在微观过程和复杂系统中揭示出了许多新的不确定性现象,致使一些论者提出各种折衷性的弱决定论观点。然而我们通过对于各种新不确定性现象的实质分析表明,它们都不真是"本质上的偶然性",并不否定决定论的严格存在;而且人也并没有超越神经生理活动的自由意志。真正使决定论不能成立的乃是世界的无限性:无限世界是不能完成综合的,故而不是一个可总括的整体或系统,因果规律对其不能适用;同时,无限世界中的有限系统和事物也因会有无限的外来影响因素而不能有决定论的未来定数。     

Comparison of permutation methods for the partial correlation and partial mantel tests

This study compares empirical type I error and power of different permutation techniques that can be used for partial correlation analysis involving three data vectors and for partial Mantel tests. The partial Mantel test is a form of first-order partial correlation analysis involving three distance matrices which is widely used in such fields as population genetics, ecology, anthropology, psychometry and sociology. The methods compared are the following: (1) permute the objects in one of the vectors (or matrices); (2) permute the residuals of a null model; (3) correlate residualized vector 1 (or matrix A) to residualized vector 2 (or matrix B); permute one of the residualized vectors (or matrices); (4) permute the residuals of a full model. In the partial correlation study, the results were compared to those of the parametric t-test which provides a reference under normality. Simulations were carried out to measure the type I error and power of these permutatio methods, using normal and non-normal data, without and with an outlier. There were 10 000 simulations for each situation (100 000 when n = 5); 999 permutations were produced per test where permutations were used. The recommended testing procedures are the following:(a) In partial correlation analysis, most methods can be used most of the time. The parametric t-test should not be used with highly skewed data. Permutation of the raw data should be avoided only when highly skewed data are combined with outliers in the covariable. Methods implying permutation of residuals, which are known to only have asymptotically exact significance levels, should not be used when highly skewed data are combined with small sample size. (b) In partial Mantel tests, method 2 can always be used, except when highly skewed data are combined with small sample size. (c) With small sample sizes, one should carefully examine the data before partial correlation or partial Mantel analysis. For highly skewed data, permutation of the raw data has correct type I error in the absence of outliers. When highly skewed data are combined with outliers in the covariable vector or matrix, it is still recommended to use the permutation of raw data. (d) Method 3 should never be used.