安徽省金融发展与经济增长的因果关系检验

文章从实证角度研究了安徽省金融发展与经济增长的关系,得出两个主要结论:一是安徽省金融发展是经济增长的格兰杰原因,是单方向的,即金融发展推动了经济增长而经济增长没有促进金融的发展;二是资本市场还不规范,它不构成经济增长的显著影响因素。通过对安徽省经济运行特点的分析,对产生结论的原因给出了合理解释并且提出了相应的政策建议。     

Granger因果关系检验的模型选择与相关策略研究

运用Granger因果关系检验识别确定经济变量间因果关系是经济研究中极为常见的分析模式,然而在具体应用时,Granger因果关系检验的功效会受到模型形式选择与检验策略因素的影响,为此,解析了Granger因果关系检验的水平型VAR、差分型VAR、VEC三种模型形式选择的基本原理,探讨了与模型选择相关的四大检验策略,即变量个数选择、滞后阶数选择、变量单整性检验、协整空间维数选择,并给出了Granger因果关系检验相对稳妥的实践操作程序。     

Granger Causality Test in the Presence of Spillover Effects

In this article, we investigate the effect of spillover (i.e., causality in variance) on the reliability of Granger causality test based on ordinary least square estimates. We studied eight different versions of the test both, with and without Whites heteroskedasticity consistent covariance matrix (HCCME). The properties of the tests are investigated by means of a Monte Carlo experiment where 21 different data generating processes (DGP) are used and a number of factors that might affect the test are varied. The result shows that the best choice to test for Granger causality under the presence of spillover is the Lagrange Multiplier test with HCCME.     

格兰杰伪因果关系检验的小样本性质研究

基于MC模拟方法研究了格兰杰伪因果关系的小样本性质,结果表明伪因果关系的发生概率会随着数据过程持久性的增强而增大,但会随着样本容量的增加而减少,且由于检验式的设定使得经Newey-West修正的检验方法并没有明显优势。通过解释变量和被解释变量的持久性对伪因果关系的影响以及与OLS估计的伪回归比较分析,表明随机干扰项的自相关或异方差是产生伪因果关系的主要原因,这为解决伪回归和伪因果关系问题提供了统一研究框架。     

A Nonparametric Bootstrap Test for the Equality of Coefficients of Variation

The problem of testing the equality of coefficients of variation for two different populations leads to various levels of difficulty depending on the possible assumptions on models and parameters for the two populations under study. Simulation techniques appear to be the only feasible way in the case where the available information came only from data and when these do not allow one to make any assumption on the models. In this work we propose a nonparametric bootstrap procedure, both to build the test statistic and also to approximate the p-value. The properties of the test and its critical aspects are illustrated and discussed by means of an application to a real data set of anthropometric measures for the study of the sexual dimorphism.     

Finite Sample Modifications of the Granger Non Causality Test in Cointegrated Vector Autoregressions

This article deals with the Granger non causality test in cointegrated vector autoregressive processes. We propose a new testing procedure that yields an asymptotically standard distribution and performs well in small samples by combining the standard Wald test and the generalized inverse procedure. We also propose a few simple modifications to the test statistics in order to help our procedure perform better in finite samples. Monte Carlo simulations show that our procedure works better than the conventional approach.     

A Flexible Nonparametric Test for Ordered and Umbrella Alternatives

A flexible and robust test for the ordered and umbrella alternatives is proposed in this paper. A two-step procedure is presented to make the proposed test be easily applicable. Type I error and power of the given approach are thoroughly investigated by extensive Monte Carlo studies.     

A Nonparametric Test Procedure Based on Quantiles for the Right Censored Data

In this study, we propose nonparametric tests using the several quantile statistics simultaneously for the right censored data. First of all, we consider statistics of the quadratic form with estimated covariance matrices. Then we derive the limiting distribution using the large sample approximation theory. Also we consider different forms of statistics such as the maximal and summing types with their limiting distributions. Then we illustrate our procedure with examples and compare performance among tests with empirical powers through a simulation study. Also we comment briefly on some interesting features including re-sampling methods as concluding remarks. Finally in Appendices, we provide proofs for the theoretic results needed for the derivation of the limiting distributions of the proposed test statistics.     

A Wavelet-Based Approach of Testing for Granger Causality in the Presence of GARCH Effects

The size and power of the most commonly used tests and a new wavelet-based approach of testing for Granger causality is evaluated by means of a Monte Carlo study in which the error term follows a generalized autoregressive conditional heteroscedasticity consistent (GARCH) process. In the simulation study it is shown that the commonly used causality tests tend to overreject the true null hypothesis in the presence of GARCH errors and that the new wavelet-based approach improves the size properties of the Granger causality test for all of the different situations evaluated.     

我国费雪效应的非参数检验

本文基于我国1990:01—2007:04期间的名义利率与通货膨胀率月度数据非线性变化的特征,应用非参数单位根和非参数协整理论检验我国是否存在费雪效应, 进而应用非参数局部线性变窗宽估计计算我国的费雪系数。由此产生的结论为：第一,非参数单位根检验发现我国名义利率与通货膨胀率都是非平稳的单位根过程;第二,非参数协整检验的结论为, 我国名义利率与通胀变化率之间存在长期的非线性协整关系, 这一结论表明我国至少存在弱的费雪效应;第三,非参数局部线性变窗宽估计计算的费雪效应(系数)的均值为0.4055,这一结果进一步支持我国存在弱的费雪效应,其隐含的意义为,当前加息对稳定通胀将产生正面效应,进一步, 如适时适度的调整利率, 很可能抑制当前较高的CPI向高通胀的转化。     

A Nonparametric Test Procedure Based on a Group of Quantile Tests

In this article, we consider nonparametric test procedures based on a group of quantile test statistics. We consider the quadratic form for the two-sided test and the maximal and summing types of statistics for the one-sided alternatives. Then we derive the null limiting distributions of the proposed test statistics using the large sample approximation theory. Also, we consider applying the permutation principle to obtain the null distribution. In this vein, we may consider the supremum type, which should use the permutation principle for obtaining the null distribution. Then we illustrate our procedure with an example and compare the proposed tests with other existing tests including the individual quantile tests by obtaining empirical powers through simulation study. Also, we comment on the related discussions to this testing procedure as concluding remarks. Finally we prove the lemmas and theorems in the appendices.     

A New Ridge Regression Causality Test in the Presence of Multicollinearity

The VAR lag structure applied for the traditional Granger causality (GC) test is always severely affected by multicollinearity due to autocorrelation among the lags. Therefore, as a remedy to this problem we introduce a new Ridge Regression Granger Causality (RRGC) test, which is compared to the GC test by means of Monte Carlo simulations. Based on the simulation study we conclude that the traditional OLS version of the GC test over-rejects the true null hypothesis when there are relatively high (but empirically normal) levels of multicollinearity, while the new RRGC test will remedy or substantially decrease this problem.     

Tests for Symmetric Error Distribution in Linear and Nonparametric Regression Models

In linear and nonparametric regression models, the problem of testing for symmetry of the distribution of errors is considered. We propose a test statistic which utilizes the empirical characteristic function of the corresponding residuals. The asymptotic null distribution of the test statistic as well as its behavior under alternatives is investigated. A simulation study compares bootstrap versions of the proposed test to other more standard procedures.     

STUR过程下危机“传染性”计量检验的有效性研究

文章首先介绍了危机“传染性”计量检验的两种分析框架:基于潜因子的分析模型和结构回归分析模型,同时给出了一些具体的检验方法,主要包括:Forbes和Rigobon的可调节的相关检验和多元“邹”检验,Favero和Giavazzi的“异常点”检验,Pesaran和Pick的阀值检验等。为检验这些方法的有效性,采用随机单位根过程作为经济数据的变迁过程,发现“异常点”检验和阀值检验可以更好地捕捉到危机传染的时间特征。     

一类基于非参数回归的条件异方差检验

现有基于参数模型构造的条件异方差检验往往存在模型设定偏误问题。为了避免模型误设对检验结果的影响,并且同时捕获多种条件异方差现象,本文基于非参数回归构造了不依赖于特定模型形式的条件异方差检验统计量。该统计量可视作条件方差和无条件方差之间差异的加权平均,在原假设成立时渐近服从标准正态分布。数值模拟结果一方面表明本文统计量具有良好的有限样本性质,另一方面也说明条件均值模型误设会导致错误地拒绝条件同方差的原假设,凸显了本文引入非参数方法构造条件异方差检验的必要性。实证分析采用本文统计量探讨了国际主要股指收益率的条件异方差现象,得到了与Engle (1982)不同的检验结果,可能意味着股指收益率呈现出非线性动态特征。     

Test for Trend With a Multinomial Outcome

ABSTRACT

There is no established procedure for testing for trend with nominal outcomes that would provide both a global hypothesis test and outcome-specific inference. We derive a simple formula for such a test using a weighted sum of Cochran–Armitage test statistics evaluating the trend in each outcome separately. The test is shown to be equivalent to the score test for multinomial logistic regression, however, the new formulation enables the derivation of a sample size formula and multiplicity-adjusted inference for individual outcomes. The proposed methods are implemented in the R package multiCA.     

A New Nonparametric Test for the Umbrella Alternative Problem

In this article, we propose a new nonparametric test for detecting umbrella alternatives. It is designed to improve the power of Pan (1996 Pan , G. ( 1996 ). Distribution-free tests for umbrella alternatives . Communications in Statistics—Theory and Methods 25 ( 12 ): 3185 – 3194 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar])'s test, which is based on the breakdown of umbrella alternatives into a union of simple-ordered alternatives. Distribution-free tests are proposed in the case where the peak of the umbrella is unknown. Some actual data examples and the results of a Monte Carlo power study are also discussed.     

A Nonparametric Test for Autoregressive Conditional Heteroscedasticity: A Markov-Chain Approach

In this article we propose a nonparametric test for autoregressive conditional heteroscedasticity based on finite-state Markov chains. A simple Monte Carlo experiment suggests that in finite samples it performs comparably to the Lagrange multiplier test under conditional normality and is superior for the t, lognormal, and exponential distributions. As an illustration, we apply both tests to Canadian/U.S. forward foreign exchange data.     

Nonparametric tests in small unbalanced samples: Application in employment-discrimination cases

Unbalanced-size samples arise naturally in equal-employment cases, as the minority fraction of all employees or applicants are invariably less than one half. Motivated by an actual case in which the median test with no power to detect disparate treatment was accepted in court, we develop a symmetrized form of the control median test having the same asymptotic properties as the median test. Since the actual case concerned the relative merits of the median and Wilcoxon test, a Monte Carlo study of the power of the new test and other nonparametric tests is reported. The results show that the new procedure is more powerful than the ordinary median test in small unbalanced samples. When the data come from a normal or double-exponential law, the Wilcoxon test is however usually superior to either of the others. When the data come from a Cauchy distribution, on the other hand, the powers of the procedures typically are reversed.     

A Nonparametric Test for Deviation from Randomness with Applications to Stock Market Index Data

The proposed test detects deviations from randomness, without a priori distributional assumption, when observations are not independent and identically distributed (i.i.d.), which is suitable for our motivating stock market index data. Departures from i.i.d. are tested by subdividing data into subintervals and then using a conditional probability measure within intervals as a binomial test. This nonparametric test is designed to detect deviations of neighboring observations from randomness when the dataset consists of time series observations. Simulation results and a comparison with Lo and MacKinlay's (1988 Lo, A. W. and MacKinlay, A. C. 1988. Stock market prices do not follow random walks: Evidence from a simple specification test. The Review of Financial Studies, 1: 41–66. [Crossref], [Web of Science ®] , [Google Scholar]) variance ratio test showed that our proposed test is a competitive alternative.