货币政策传导机制有效性的实证研究

文章选取货币供应量和金融机构年末贷款余额作为货币政策的中介指标变量,以经济增长作为货币政策的最终目标变量,运用动态计量经济学理论,通过对变量的单位根检验,协整检验和因果关系检验,对1978~2004年的数据采用EG两步法建立误差修正模型。研究表明货币政策传导存在明显的时滞,而且货币供应量增长率与贷款余额增长率相比对经济增长率的影响更显著。     

个体价值追求对组织认同影响模型

应用开发的量表调查得到的数据,构建个体价值追求对组织认同心理的影响结构方程,运用结构方程建模(SEM)进行检验,得到个体价值追求因素对组织认同影响模型,拟合结果比较满意,指标效度和潜变量的建构信度较高,分析了个体价值追求诸因素对组织认同的影响,获得了定量分析结论.     

中国大宗进口商品价格变化对CPI的影响——以粮食、原油和钢材为例

CPI是衡量通货膨胀水平的重要指标之一.随着经济的发展,中国对外贸易的依赖性逐步提高,若干资源性商品需求增长,大宗商品消费需求增长率有所提高,而近年来,中国大宗进口商品的价格不断提高,对CPI的变化产生了显著的影响.本文通过采用变量平稳性检验、格兰杰因果检验等方法对数据进行分析,研究了大宗进口商品的价格变化对CPI的影响情况,并提出了若干对策.     

一类新的多重共线性检验方法

解释变量间的相关性导致了多元线性回归模型的多重共线性问题,由于考察相关性的角度和方法不同,产生了不同的多重共线性的检验方法。由阿达马不等式可以构建多个变量的综合相关性度量指标,将该指标用于度量多元线性回归模型的解释变量的综合相关程度,用以作为多元线性回归模型多重共线性的一类检验方法。     

中国农产品期货价格指数与宏观经济变量波动关系分析

在考察农产品期货价格指数与宏观经济变量关系时,已有研究主要使用Granger因果关系分析及时差相关系数检验农产品期货价格指数到宏观经济变量是否具有Granger因果关系以及先行期为多少,文章则采用建立VAR模型的方法,通过脉冲响应和方差分解考察农产品期货价格指数的随机变动对宏观经济变量波动的影响以及农产品期货价格指数对宏观经济变量波动的贡献率.同时,建立VEC模型考察农产品期货价格指数对宏观经济变量的长期和短期影响.     

高校教师工资影响因素分析

文章以某地区若干高校教师月平均工资为研究对象,随机抽取500名教师为样本,对于一些定性的解释变量采用虚拟变量的形式与工资进行回归分析,通过拟合线性回归模型及对其参数和整体方程的检验,分析出年龄、学历、职务等定性的解释变量对高校教师工资的影响程度。     

流动性过剩对期货市场影响的实证研究

文章选取M2同比增长率为流动性指标,并转化为虚拟变量,建立含有流动性指标的GARCH-GED模型对上海期货交易所的金属铜和天然橡胶进行检验,并运用小渡db(4)对波动序列进行小渡分解.实证结果表明,超额货币供应量会加剧期货市场的波动,从宏观和微观两角度验证了流动性过剩对期货市场的影响是显著的.     

边限检验理论及几点讨论

检验经济变量之间长期关系的协整技术要求变量是同阶单整的,这不可避免地涉及一定程度的预检验问题,而预检验问题会增加变量间长期关系分析的不确定性。当不能确定变量的单整类型时,边限检验理论提出了一个可以直接检验一个变量和一组解释变量之间长期关系的新方法。在介绍了边限检验方法中基本的VAR模型和假设及边限检验方法中用到的重要统计量——Wald统计量和T统计量及它们各自的渐近分布形式后,说明了边限检验理论在理论和实际运用当中需要注意的几个问题,最后通过实例分析说明了边限检验理论的运用。     

中国国际收支经常账户与资本金融账户关系研究：1981-2007

 本文针对中国国际收支多年来“双顺差”的事实,利用1981～2007年度中国国际收支时序数据和我们认为与之相关的最重要的两个宏观经济变量：中国的对外开放度指标和实际GDP的增长率数据,构建了多组向量自回归（VAR）模型,通过格兰杰因果检验、脉冲响应函数和方差分解等计量方法,实证检验了中国国际收支经常账户及其子账户与资本金融账户之间,各账户与宏观经济变量之间的因果关系、动态冲击响应和变量间影响的相对重要性。     

上市公司内部控制审计与融资成本的关联性分析

文章以中国沪深股市231家上市公司为分析样本,基于Easton模型计算权益融资成本,对内部控制审计与融资成本之间的关联性进行了分析.通过直观性描述分析了各变量对融资成本的影响,通过相关性分析进行显著性检验并获得了影响融资成本的关键因素,最后通过多元变量回归分析对构建模型的两个假设进行了验证.     

How effective are the reset tests for omitted variables

In regression analysis, RESET has widely been regarded as an effective diagnostic test especially for omitted variables. This paper investigates the limitations of the existing RESET tests in detecting omitted variables. We analyze the sources from which RESET draws its power and point out the circumstances under which RESET will likely be ineffective. We offer some Monte Carlo evidence as well as an empirical application to illustrate the weaknesses of the RESET tests. A more robust RESET type test is proposed.     

Granger-causal analysis of GARCH models: A Bayesian approach

A multivariate GARCH model is used to investigate Granger causality in the conditional variance of time series. Parametric restrictions for the hypothesis of noncausality in conditional variances between two groups of variables, when there are other variables in the system as well, are derived. These novel conditions are convenient for the analysis of potentially large systems of economic variables. To evaluate hypotheses of noncausality, a Bayesian testing procedure is proposed. It avoids the singularity problem that may appear in the Wald test, and it relaxes the assumption of the existence of higher-order moments of the residuals required in classical tests.     

A Test of Independence in Two-Way Contingency Tables Based on Maximal Correlation

Maximal correlation has several desirable properties as a measure of dependence, including the fact that it vanishes if and only if the variables are independent. Except for a few special cases, it is hard to evaluate maximal correlation explicitly. We focus on two-dimensional contingency tables and discuss a procedure for estimating maximal correlation, which we use for constructing a test of independence. We compare the maximal correlation test with other tests of independence by Monte Carlo simulations. When the underlying continuous variables are dependent but uncorrelated, we point out some cases for which the new test is more powerful.     

Sensitivity analysis for two control groups

In many experiments where data have been collected at two points in time (pre-treatment and post-treatment), investigators wish to determine if there is a difference between two treatment groups. In recent years it has been proposed that an appropriate statistical analysis to determine if treatment differences exist is to use the post-treatment values as the primary comparison variables and the pre-treatment values as covariates. When there are several outcome variables, we propose new tests based on residuals as alternatives to existing methods and investigate how the powers of the new and existing tests are affected by various choices of covariates. The limiting distribution of the test statistic of the new test based on residuals is given. Monte Carlo simulations are employed in the power comparisons.     

Powerful Parametric Tests Based on Sum‐Functions of Spacings

Assume that we have a sequence of n independent and identically distributed random variables with a continuous distribution function F, which is specified up to a few unknown parameters. In this paper, tests based on sum‐functions of sample spacings are proposed, and large sample theory of the tests are presented under simple null hypotheses as well as under close alternatives. Tests, which are optimal within this class, are constructed, and it is noted that these tests have properties that closely parallel those of the likelihood ratio test in regular parametric models. Some examples are given, which show that the proposed tests work also in situations where the likelihood ratio test breaks down. Extensions to more general hypotheses are discussed.     

对外贸易影响中国经济增长的动态分析

运用计量经济学方法对中国1980-2007年相关经济变量时间序列数据的平稳性进行ADF检验,对修正后表现平稳的变量进行协整分析,研究在长期内是否存在稳定的均衡关系。通过构建协整方程、误差修正模型和Granger因果关系式分析各经济变量在长期均衡和短期波动中相互因果关系的影响。研究结果表明：在短期内经济增长更多受制于需求,而长期内取决于生产效率的提高。因此在继续重视出口贸易创造市场作用的前提下,要充分发挥外贸在增加要素供给和创造市场需求两个方面的作用。     

空间动态面板模型的时间滞后效应检验

空间面板数据模型由于考虑了经济变量间的空间相关性,其优势日益凸显,已成为计量经济学的热点研究领域。将空间相关性与动态模式同时扩展到面板模型中的空间动态面板模型,不仅考虑了经济变量之间的空间相关性,还考虑了时间上的滞后性,是空间面板模型的发展,增强了模型的解释力。考虑一种带固定个体效应、因变量的时间滞后项、因变量与随机误差项均存在空间自相关性的空间动态面板回归模型,提出了在个体数n和时间数T都很大,且T相对地大于n的条件下空间动态面板模型中时间滞后效应存在性的LM和LR检验方法,其检验方法包括联合检验、一维及二维的边际和条件检验;推导出这些检验在零假设下的极限分布;其极限分布均服从卡方分布。通过模拟试验研究检验统计量的小样本性质,结果显示其具有优良的统计性质。     

General Model for Reliability-Based Engineering Design

This article applies general engineering rules for describing the reliability of devices working under variable stresses. The approach is based on imposing completeness and physicality. Completeness refers to the model's capability for studying as many stated conditions as possible, and physicality refers to the model's capability for incorporating explanatory variables specified and related each other by the physical laws. The proposed reliability model has as many explanatory variables as necessary but only three unknown parameters, and hence, it allows the engineer to collect reliability data from different tests campaigns, and to extrapolate reliability results towards other operational and design points.     

On the existence of transformations preserving the structure of order statistics in lower dimensions

In a Type-II right censored sample from the standard uniform distribution, several transformations of respective order statistics are examined, which transform the censored sample into a complete sample in a lower dimension. Such transformations have been considered by Lin et al. (2008), Michael and Schucany (1979) and O’Reilly and Stephens (1988) in the context of goodness-of-fit tests. It is shown that by dropping the assumption of an underlying uniform distribution, these transformed random variables can no longer be considered themselves as order statistics, in general. In the case of the transformation of Michael and Schucany, it is shown that the uniform distribution is the only one possessing this property.     

Multivariate generalizations of jonckheere's test for ordered alternatives

In many dose-response studies, each of several independent groups of animals is treated with a different dose of a substance. Many response variables are then measured on each animal. The distributions of the response variables may be nonnormal, and Jonckheere's (1954) test for ordered alternatives in the one-way layout is sometimes used to test whether the level of a single variable increases with increasing dose. In some applications, however, it is important to consider a set of response variables simultaneously. For instance, an increase in each of certain enzymes in the blood serum may suggest liver damage. To test whether these enzyme levels increase with increasing dose, it may be preferable to consider these enzymes as a group, rather than individually.

I propose two multivariate generalizations of Jonckheere's univariate test. Each multivariate test statistic is a function of coordinate-wise Jonckheere statistics—one a sum, the other a quadratic form. The sum statistic can be used to test the alternative hypothesis that each variable is stochastically increasing with increasing dose. The quadratic form statistic is designed for the more general alternative hypothesis that each variable is stochastically ordered with increasing dose.

For each of these two alternatives, I also propose a multivariate generalization of a normal theory test described by Puri (1965). I examine the asymptotic distributions of the four test statistics under the null hypothesis and under translation alternatives and compare each distribution-free test to the corresponding normal theory test in terms of asymptotic relative efficiency.

The multivariate Jonckheere tests are illustrated using does-response data from a subchronic toxicology study carried out by the National Toxicology Program. Four groups of ten male rats each were treated with increasing doses of vinylidene flouride, and the serum enzymes SDH, SGOT, and SGPT were measured. A comparison of univariate Jonckheere tests on each variable, bivariate tests on SDH and SGOT, and multivariate tests on all three variables gives insight into the behavior of the various procedures.