定量数据分析效应值研究综述

近20年来,国外对定量数据分析效应值的研究日趋丰富。总结这一阶段国内外效应值研究的成果,主要包括效应值研究意义、效应值分类、计算方法和解释标准、效应值报告与解释研究现状、国内效应值研究现状。在此基础上,指出效应值研究是有待进一步探讨的课题,并提出改善效应值研究现状的建议。     

基于高频数据的股票市场价格集聚效应特征研究

股票价格常常呈现出集聚效应，并对市场质量产生重要影响，因此需要利用高频数据深入研究中国股票市场价格集聚效应的存在性及其表现特征，同时解释其原因。通过对沪市高频交易数据的统计检验和定量实证研究表明：价格尾数在“0”、“5”和“8”上存在显著的集聚效应，并且可以用“价格决定”假说加以解释，但是不能用“价格吸引”假说解释。投资者通过在这些点位上变动一个最小报价单位进行报价，将可以最小成本获得交易的优先权。     

产业结构升级对城乡收入差距的门槛效应分析

综合借鉴前人的研究成果,本文将产业结构升级、城镇化进程和城乡收入差距三者联系起来,初步判断产业结构升级对城乡收入差距的影响是非线性的;然后采用2005-2018年我国省级面板数据,以城镇化率为门槛变量,对此进行了实证检验。结果显示:产业结构升级与城乡收入差距之间存在明显的非线性关系,前者对后者的影响具有显著的双门槛效应,当城镇化水平处于初级阶段时,产业结构合理化和高级化扩大了城乡收入差距;当城镇化水平越过第一门槛值进入中级阶段后,产业结构合理化的影响减弱为不显著,而产业结构高级化仍在显著扩大城乡收入差距;当城镇化水平越过第二门槛值进入高级阶段后,产业结构合理化和高级化都表现为显著缩小城乡收入差距。通过剔除样本中的极端值、替换被解释变量和核心解释变量的代理变量,进行稳健性检验,上述结论依然成立。另外,分地区来看,东部、东北和西部地区的产业结构升级对城乡收入差距的影响同样存在显著的双门槛效应,但不同区域的表现不尽相同,东部和东北地区的门槛效应均表现为倒“U”型;西部地区则“慢半拍”,尚处于倒“U”型曲线的左侧;中部地区则仅存在以城镇化率为门槛变量的单门槛效应。文章分析了其中的原 因,并给出了政策含义。     

我国经济增长与金融深度的关系检验

文章运用协整检验和格兰杰因果检验方法,研究了我国经济增长与金融深度的关系。协整检验结果表明我国的经济增长与金融深度之间不存在长期稳定的均衡关系;格兰杰因果检验结果表明我国的经济增长与金融深度之间存在着单向的由经济增长指向金融深度的因果关系。并且,首次运用阈值效应对此结果给出解释。     

基于EGARCH-M模型的沪市价量关系的实证研究

本文基于MDH理论,借助EGARCH-M模型,实证检验了上海股市的价量关系。结果表明非预期交易量对股市波动有很强解释能力,而预期交易量对股市波动几乎没有解释能力。同时还揭示了上海股市具有一定有效性,且存在杠杆效应。     

面板数据模型中随机效应存在性检验的理论研究及其实证分析

 由于能体现异质性等一系列优良性质,面板数据模型正被广泛应用到经济学各个领域中。然而,在反映异质性的个体效应和时间效应的设定上,经常存在人为的主观性和随意性,因此容易导致错误指定事件的发生。本文提出了一个稳健的方法分别检验面板数据模型中随机个体效应和随机时间效应的存在性。具体而言,通过对残差进行正交化变换消去可能存在的时间效应,并建立人工自回归模型,然后基于该模型自回归系数的最小二乘估计构造检验统计量检验个体效应。构造的检验是单边的,零假设下渐近服从标准正态分布。在检验时间效应时,可类似得到统计量及其渐近性质。功效研究表明这些检验敏感性较强,能检测到以参数速度（最快的速度）收敛到零假设的备择假设。通过模拟试验研究了检验统计量的小样本性质,并进行了实际数据分析。     

转型期中国省际经济波动对经济增长的空间溢出效应

本文基于中国31个省市1979-2012年的数据,采用动态空间杜宾模型,实证分析转型期中国省际经济波动对经济增长的空间溢出效应及其形成机制,得出结论如下:经济波动在淘汰本地区低效率投资项目和企业、优化投资结构、从而对本地区经济增长产生正向直接效应的基础上,因受中国地区间激烈竞争和产业同构的影响,会吸引邻近地区的优质资源,对这些地区的投资结构产生不利影响,进而对经济增长产生负向空间溢出效应.并且,从动态视角的检验发现,相比于短期,两种效应在长期的作用更大.本文关于经济波动对经济增长空间溢出效应的研究对于解释中国经济波动与经济增长的关系机理,以及对于区域经济协同发展、加快国内市场一体化相关政策的制定具有重要意义.     

习惯形成下中国城乡居民预防性储蓄研究

本文将习惯偏好引入传统的预防性储蓄模型,构造出检验具有习惯偏好居民的预防性储蓄动机的计量方程。然后利用中国26个省1999-2011年城乡居民消费数据进行实证分析。回归结果发现,在控制了习惯形成效应后,城乡居民依然存在显著的预防性储蓄动机,但是该动机强度比无习惯偏好时降低了约1/2。同时,城乡对比结果表明,在绝对层面,农村居民具有更大的衡量预防性储蓄动机强度的绝对谨慎系数,其值约为城镇居民的1.5倍;然而基于现有消费,农村居民衡量预防性储蓄量的相对谨慎系数则要小于城镇居民,约为后者的60%。本文最后在城乡二元结构背景下对上述结果进行解释,并给出相应的政策建议。     

基于多重多元回归的多目标影响因素效应比较及应用——以企业经营活动分析为例

企业运营管理活动的结果可能表现在多个方面,这些结果的产生会受到若干个因素的影响。影响因素及其组合对企业运营管理结果的作用效果是否存在差异,从计量角度加以测算分析,有助于企业资源的合理配置和利用。运用统计回归分析的基本原理,讨论了多个影响因素及其组合对企业经营多个目标回归效应差异问题,包括:不同组解释变量对同一被解释变量回归效应的比较,同一组解释变量对不同被解释变量回归效应的比较,不同组解释变量对所有被解释变量回归效应的比较,并结合事实数据进行了应用说明。     

FDI对中国碳排放强度影响的门槛效应检验

文章以1997-2015年中国30个地区的省际面板数据为样本,构建普通面板基准模型,实证检验了FDI对我国碳排放强度的影响,结果表明:FDI对我国碳排放强度的影响存在显著的区域差异;在东部和中部地区FDI对碳排放强度的影响表现出“污染光环效应”,且东部大于中部;在西部地区则表现出显著的“污染天堂效应”;在此基础上,构建门槛模型就FDI对我国碳排放强度影响的区域差异进行非线性的门槛效应检验.结果表明,在不同的经济发展、人力资本、环境规制、金融发展和对外开放水平下,FDI对我国碳排放强度的影响存在显著的非线性门槛效应,当门槛变量越过相应的门槛值时,有利于FDI降低碳排放强度作用的发挥.     

A comparison of the type I error rates of three assessment methods for indirect effects

Mediation analysis is a popular statistical analysis verifying the relation between an independent variable and a dependent variable through a mediator. There are three traditional tests to assess indirect effects: the Baron and Kenny test (BK), the Sobel test (ST) and the bootstrap method (BT). Previous studies have showed that the BT is more powerful and more conceptually appropriate. However, no study has systematically compared these tests regarding the type I error rate. A Monte-Carlo simulation is carried out with 19 scenarios varying paths (but no indirect effect), 9 scenarios varying the direct effect, and 6 sample sizes (1056 different scenarios). Results show that the BT had an overall good performance even for small sample size and whatever the effect sizes. The ST and the BK test were conservative, especially with small sample size and low effect sizes. In conclusion, these tests should be avoided, and the BT is recommended.     

Bias in meta-analysis due to outcome variable selection within studies

Although bias in meta-analysis arising from selective publication has been studied, within-study selection has received little attention. Chronic diseases often have several possible outcome variables. Methods based on the size of the effect allow results from studies with different outcomes to be combined. However, the possibility of selective reporting of outcomes must be considered. The effect of selective reporting on estimates of the size of the effect and significance levels is presented, and sensitivity analyses are suggested. Substantial publication bias could arise from multiple testing of outcomes in a study, followed by selective reporting. Two meta-analyses, on anthelminth therapy and a treatment for incontinence, are reassessed allowing for within-study selection, as it is clear that more outcomes had been measured than were reported. The sensitivity analyses show that the robustness of the anthelminth results is dependent on what assumption one makes about the reporting strategy for the largest trial. The possible influence of correlation between within-child measurements was such that the conclusions could easily be reversed. The effect of a mild assumption on within-trial selection alone could alter general recommendations about the treatment for incontinence.     

A Survey regarding the Reporting of Simulation Studies

Hoaglin and Andrews (1975) proposed standards for computational practice and the reporting of computation-based studies. They observed that “statisticians … often pay too little attention to their own principles of design.” To see if the design and reporting had improved, we surveyed five major statistical journals for 1975, 1978, and 1981 to ascertain whether reported simulation studies involved a specified design, justified the choice of the number of iterations, and specified the random number generator(s) used. Eighteen percent of the 1,198 papers surveyed included results based on simulation. We found that 9% of the papers including a simulation study justified the choice of the number of iterations and 44% at least partially specified the random number generator. Hoaglin and Andrews's observation appears still to be true.     

Estimation method of multivariate exponential probabilities based on a simplex coordinates transform

A novel unbiased estimator for estimating the probability mass of a multivariate exponential distribution over a measurable set is introduced and is called the exponential simplex (ES) estimator. For any measurable set and given sample size, the statistical efficiency of the ES estimator is higher than or equal to the statistical efficiency of the well-known Monte Carlo (MC) estimator. For non-radially shaped measurable sets, the ES estimator has a strictly higher statistical efficiency than the MC estimator. For ray-convex sets, such as convex sets, the ES estimator can be expressed in a simple analytical form.     

Estimating test power adjusted for size

Statisticians seek tests which have maximum power amongst tests of size α. In both numerical and theoretical studies, the standard approach is to compare the powers of competing tests which have the same nominal size α*. In most cases, α and α* differ; and in this case, the differing size biases of the tests contaminate any comparisons of their power. For instance, two nominal 5% tests with actual sizes 4% and 6% should not have their powers naively compared. In this paper, the basic problem of trading-off size for power is approached through the existing theory of receiver operating characteristic curves. This leads us to a simple way of estimating power adjusted for size, not only for a fixed nominal size, but also for a range of relevant nominal sizes. The calculations required are both familiar and simple. We recommend that the methods be routinely applied to simulations studies that compare alternative tests of the same hypotheses.     

Practical usage of O'Brien's OLS and GLS statistics in clinical trials

Multivariate techniques of O'Brien's OLS and GLS statistics are discussed in the context of their application in clinical trials. We introduce the concept of an operational effect size and illustrate its use to evaluate power. An extension describing how to handle covariates and missing data is developed in the context of Mixed models. This extension allowing adjustment for covariates is easily programmed in any statistical package including SAS. Monte Carlo simulation is used for a number of different sample sizes to compare the actual size and power of the tests based on O'Brien's OLS and GLS statistics.     

Putting the P-Value in its Place

ABSTRACT

As the debate over best statistical practices continues in academic journals, conferences, and the blogosphere, working researchers (e.g., psychologists) need to figure out how much time and effort to invest in attending to experts' arguments, how to design their next project, and how to craft a sustainable long-term strategy for data analysis and inference. The present special issue of The American Statistician promises help. In this article, we offer a modest proposal for a continued and informed use of the conventional p-value without the pitfalls of statistical rituals. Other statistical indices should complement reporting, and extra-statistical (e.g., theoretical) judgments ought to be made with care and clarity.     

Measuring Nonlinear Granger Causality in Mean

We propose model-free measures for Granger causality in mean between random variables. Unlike the existing measures, ours are able to detect and quantify nonlinear causal effects. The new measures are based on nonparametric regressions and defined as logarithmic functions of restricted and unrestricted mean square forecast errors. They are easily and consistently estimated by replacing the unknown mean square forecast errors by their nonparametric kernel estimates. We derive the asymptotic normality of nonparametric estimator of causality measures, which we use to build tests for their statistical significance. We establish the validity of smoothed local bootstrap that one can use in finite sample settings to perform statistical tests. Monte Carlo simulations reveal that the proposed test has good finite sample size and power properties for a variety of data-generating processes and different sample sizes. Finally, the empirical importance of measuring nonlinear causality in mean is also illustrated. We quantify the degree of nonlinear predictability of equity risk premium using variance risk premium. Our empirical results show that the variance risk premium is a very good predictor of risk premium at horizons less than 6 months. We also find that there is a high degree of predictability at the 1-month horizon, that can be attributed to a nonlinear causal effect. Supplementary materials for this article are available online.