外国直接投资与我国经济增长的相关性分析

从20世纪80年代开始,外国直接投资(FDI)在一国经济与贸易的发展中起着越来越重要的作用。本文应用协整关系检验和Granger因果关系检验法,研究了我国经济增长与外商直接投资之间的关系,并对FDI对GDP贡献率做了经济计量分析。     

外商直接投资对我国GDP贡献的实证研究

本文在经济增长理论框架的基础上纳入外商直接投资变量,着重就外商直接投资对中国经济增长的影响进行理论和量化分析,实证外商直接投资对我国国内生产总值贡献作用的大小,在Granger因果检验分析的基础上进一步得出外商直接投资在中国经济增长过程中的作用机制。     

外商直接投资对中国经济的影响

一、全球外国直接投资发展趋势 1.近20年全球外国直接投资呈高速增长态势.上世纪80年代以来,随着经济全球化和地区经济一体化进程的加快,全球外国直接投资(简称FDI)均以高于经济增长和贸易增长的速度增长,呈现高速增长态势.     

中国经济增长与外国直接投资的葛兰杰因果检验及协整分析

从理论上讲,一国经济增长与外国直接投资的关系是互为因果的,即外国直接投资可以促进经济增长,经济增长可以吸引外国直接投资的流入.中国自改革开放以来,利用外国直接投资的数额一直居于世界各国前列.2002年中国利用外国直接投资首次超过了美国成为全球吸引外资最多的国家.这一年,中国的外国直接投资合同金额达到828亿美元,比2001年增长了19.6%,实际金额达到527亿美元,比2001年增长了12.5%;2003年中国利用外资数额继续上涨,合同外资金额达到1150.70亿美元,同比增长39.03%;实际使用外资金额535.05亿美元,同比增长1.44%(数据来源:中华人民共和国商务部网站).     

中国经济增长的原动力

本文采用因果分析和协整检验的方法 ,运用矢量误差校正模型 (VEC模型 )来确定对外贸易和外商直接投资与GDP增长之间是否存在稳定的动态长期均衡关系 ,从而挖掘中国经济快速增长的原动力。结果表明 :对外贸易和外商直接投资与GDP增长之间存在着动态长期均衡关系 ,GDP的增长导致对外贸易和外商直接投资的增长 ,反之 ,亦然     

教育投资对经济增长的动态关联机制分析——以广西地区为例

教育投资对经济增长具有促进作用已得到公认。采用单位根检验,协整检验和Granger因果检验考察广西地区教育投资和经济增长之间的关系,结果显示：广西地区教育投资与经济增长存在着长期协整关系并建立了误差修正模型．且存在教育投资到经济增长单向的因果关系;本文建立Almon分布滞后模型对广西地区教育投资与经济增长关联机制进行了动态计量实证分析,发现广西地区教育投资在影响经济增长中存在5期的滞后效应,教育投资提高1％,在6年内共使得经济增长了0．9689％,教育投资较为明显地拉动了经济增长。     

外商直接投资与我国经济增长关系的实证分析

外商直接投资是在目前形势下迫切需要探讨的一个课题,本文对我国外商直接投资和经济增长之间的关系进行了实证分析。利用格兰杰因果关系检验和协整检验方法对我国外商直接投资与经济增长的关系进行了实证检验,在此基础上建立了适应我国实际情况的误差修正模型,并对我国外商直接投资与经济增长之间的长期均衡关系和短期动态关系进行了分析。     

外国直接投资与我国经济增长关系实证研究

改革开放以来我国在吸引外国直接投资（FDI）方面从无到有，从少到多，成为吸引外国直接投资最多的发展中国家，FDI已成为我国投资的重要来源，约占全部投资的1／5。用传统的经济计量模型研究FDI与长期经济增长关系时，我们都先验地认为二者之间具有长期均衡关系。那么，FDI是不是与我国经济增长存在长期稳定关系？FDI对中国经济的增长究竟发挥了多大的作用？二者是否相互影响？本文应用协整理论探讨了FDI对我国经济增长的效用大小。     

我国公共教育投资水平与经济总量的因果关系检验

教育投资与经济增长之间是否存在着长期稳定的因果关系,这是我国制定公共教育投资政策的重要依据之一。通过协整理论可以检验教育投资与我国经济总量指标是否存在长期稳定的比例关系,通过格兰杰因果关系检验能说明教育投资是否为我国经济增长的原因之一。     

FDI进入模式的决定因素及实证分析

FDI(外国直接投资)已经成为我国经济增长的重要因素。FDI的进入模式与投资环境和投资者对接受国政策的敏感度等因素有关。在我们的模型里,接受国的腐败水平和投资项目的技术性质直接影响了FDI的投资规模和进入模式,进而决定了"三资"企业的内部产权结构。文章并对此进行了实证检验。     

Use of the squared ranks test to test for the equality of the coefficients of variation

A distribution-free test for the equality of the coefficients of variation from k populations is obtained by using the squared ranks test for variances, as presented by Conover and Iman (1978) and Conover (1980), on the original observations divided by their respective expected values. Substitution of the sample mean in place of the expected value results in the test being only asymptotically distribution-free. Results of a simulation study evaluating the size of the test for various coefficient of variation values and probability distributions are presented.     

Accurate tests for the equality of coefficients of variation

ABSTRACT

A frequently encountered statistical problem is to determine if the variability among k populations is heterogeneous. If the populations are measured using different scales, comparing variances may not be appropriate. In this case, comparing coefficient of variation (CV) can be used because CV is unitless. In this paper, a non-parametric test is introduced to test whether the CVs from k populations are different. With the assumption that the populations are independent normally distributed, the Miller test, Feltz and Miller test, saddlepoint-based test, log likelihood ratio test and the proposed simulated Bartlett-corrected log likelihood ratio test are derived. Simulation results show the extreme accuracy of the simulated Bartlett-corrected log likelihood ratio test if the model is correctly specified. If the model is mis-specified and the sample size is small, the proposed test still gives good results. However, with a mis-specified model and large sample size, the non-parametric test is recommended.     

A monte carlo study of the friedman and conover tests in the single-factor repeated measures design

A Monte Carlo study was used to compare the Type I error rates and power of two nonparametric tests against the F test for the single-factor repeated measures model. The performance of the nonparametric Friedman and Conover tests was investigated for different distributions, numbers of blocks and numbers of repeated measures. The results indicated that the type of the distribution has little effect on the ability of the Friedman and Conover tests to control Type error rates. For power, the Friedman and Conover tests tended to agree in rejecting the same false hyporhesis when the design consisted of three repeated measures. However, the Conover test was more powerful than the Friedman test when the number of repeated measures was 4 or 5. Still, the F test is recommended for the single-factor repeated measures model because of its robustness to non-normality and its good power across a range of conditions.     

Testing the homogeneity of several two‐parameter populations

The authors extend Fisher's method of combining two independent test statistics to test homogeneity of several two‐parameter populations. They explore two procedures combining asymptotically independent test statistics: the first pools two likelihood ratio statistics and the other, score test statistics. They then give specific results to test homogeneity of several normal, negative binomial or beta‐binomial populations. Their simulations provide evidence that in this context, Fisher's method performs generally well, even when the statistics to be combined are only asymptotically independent. They are led to recommend Fisher's test based on score statistics, since the latter have simple forms, are easy to calculate, and have uniformly good level properties.     

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.     

Tests for the equality of multinomial parameters

In this paper we face the problem of testing the equality of two or more parameters of a multinomial distribution. We develop a likelihood ratio test and we consider an asymptotically equivalent Pearson's statistic. Moreover we develop an exact and a randomized test. Relationships between these tests are then discussed. The behaviour of these tests is studied by simulations. Results from two known tests developed for less general situations are compared to ours.     

Likelihood ratio test for simultaneous testing of the mean and the variance of a normal distribution

In this paper, we consider the simultaneous testing of the mean and the variance of a normal distribution. The exact distribution of the likelihood ratio test statistic is obtained, which is not available in the literature. The critical points of the exact test are reported. We also consider some of the other exact and asymptotic tests. The powers of these tests are compared using the Monte Carlo simulations.     

检验式设定错误对时间序列单位根检验小样本性质的影响

首先对单位根检验的两类常见的数据生成系统进行比较，然后利用蒙特卡洛实验研究了时间序列单位根检验式的设定问题。研究发现在利用DF检验和DF-GLS检验进行时间序列的单位根检验时，检验式设定错误直接影响着检验结果，尤其在推断时间序列是趋势平稳过程还是有时间趋势项的随机游走过程或有二阶时间趋势多项式的随机游走过程时，检验式的错误设定很容易将趋势平稳过程误判为非平稳过程。     

On the Exact Size of Tests of Treatment Effects in Multi‐Arm Clinical Trials

When testing treatment effects in multi‐arm clinical trials, the Bonferroni method or the method of Simes 1986) is used to adjust for the multiple comparisons. When control of the family‐wise error rate is required, these methods are combined with the close testing principle of Marcus et al. (1976). Under weak assumptions, the resulting p‐values all give rise to valid tests provided that the basic test used for each treatment is valid. However, standard tests can be far from valid, especially when the endpoint is binary and when sample sizes are unbalanced, as is common in multi‐arm clinical trials. This paper looks at the relationship between size deviations of the component test and size deviations of the multiple comparison test. The conclusion is that multiple comparison tests are as imperfect as the basic tests at nominal size α/m where m is the number of treatments. This, admittedly not unexpected, conclusion implies that these methods should only be used when the component test is very accurate at small nominal sizes. For binary end‐points, this suggests use of the parametric bootstrap test. All these conclusions are supported by a detailed numerical study.     

Testing the equality of several means when the population variances are unequal

A comparative study is made of three tests, developed by James (1951), Welch (1951) and Brown & Forsythe (1974). James presented two methods of which only one is considered in this paper. It is shown that this method gives better control over the size than the other two tests. None of these methods is uniformly more powerful than the other two. In some cases the tests of James and Welch reject a false null hypothesis more often than the test of Brown & Forsythe, but there are also situations in which it is the other way around.

We conclude that for implementation in a statistical software package the very complicated test of James is the most attractive. A practical disadvantage of this method can be overcome by a minor modification.