非线性Granger因果检验方法的检验功效及有限样本性质的模拟分析

为了克服传统Granger因果检验方法因忽略经济变量的非线性特征而导致结论出现显著偏差的局限性,非线性Granger因果检验方法在近年来正逐步成为经济学研究领域的重要分析工具。然而,迄今为止,学术界仍较少对非线性Granger因果检验方法在不同非线性模型中的有限样本性质展开系统性的比较与分析,因此,本文通过数据生成过程（DGP）,结合Monte Carlo模拟对Diks和Panchenko（2006）等主流的非线性Granger因果检验方法的检验功效、过度拒绝等问题展开比较研究,并对共同滞后阶数、带宽参数的不同设置可能引发结论敏感性变化进行深入分析,在此基础上我们从动态非线性滚动分析的角度对其有限样本性质展开进一步的讨论,并提出对未来非线性应用研究具有实际指导价值的若干建议。     

基于量价关系的股票价格分布模型探析

基于量价关系,用交易量的分布解释收益率的分布情况。分别探讨了预期交易量、非预期交易量与收益率之间的关系,发现交易量和收益率有明显的正相关关系;收益率对预期交易量以及非预期交易量都有解释作用,但无论是预期交易量还是非预期交易量对收益率都没有显著的解释作用,但是从统计量分布上看,前者的分布能够解释后者的分布。      

我国教育投入与经济增长关系的实证研究

利用协整分析和Granger因果检验方法研究1952-2002期间,我国教育投入和经济增长之间的相互关系,证实我国经济增长教育的投入,且教育投入与国内生产总值间互为因果关系,增加教育投入有助于促进我国实际经济总量的增长,同时,经济和增长和发展水平决定着教育投入的规模.     

产业结构与城市化互动关系的实证检验

产业结构调整和城市化是经济发展过程中的两个中心主题。文章以1978～2008年我国三次产业的产值比重、就业比重和城市化率的统计数据为样本,对产业结构与城市化之间的互动关系进行了实证研究,结果表明,我国城市化与第一、二产业的产值结构和第一、二、三产业的就业结构存在着长期均衡关系,而与第三产业的产值结构不存在长期均衡关系;城市化率与三次产业的就业比重存在着单向因果关系,城市化推进是第一、二产业就业比重变化的原因,第三产业就业比重的增加是城市化水平提高的原因。     

基于VAR模型的四川经济增长与投资消费关系实证研究

选取1990-2010年四川省经济数据,运用格兰杰因果检验、VAR模型和脉冲响应分析等方法对四川省经济增长与投资消费之间的关系进行了实证研究,结果表明：不论时间长短,投资水平对经济增长和消费水平的影响相对显著,而经济增长和消费水平在短期内对其他两项的影响不显著。     

湖南农村金融与经济增长关系的实证分析

采用格兰杰因果检验和协整方法对湖南农村金融发展与经济增长之间的关系进行实证检验,研究表明,1987-2008年间,存在农村真实货币余额增长率(M/P)、存款利率(R)、乡镇企业贷款(FL)三个因素的协整关系,其中农村真实货币余额增长率、乡镇企业贷款对农林牧渔业总产值的存在因果关系,其他序列与农林牧渔业总产值则不存在因果关系,同时利用回归分析对三因素进行定量分析。这说明湖南农村金融深化、农业借贷能在一定程度上促进农村经济的增长。     

我国货币供给与股票价格的关联性

在威廉斯估价模型和货币内生性理论框架内,通过理论分析可以看出,股票价格变化会对货币供给变化产生一定冲击,同时,货币供给变化也会对股票价格变化产生一定影响。但是,通过选取中国金融市场的历史数据进行平稳性检验和协整分析,并建立VAR模型,用SAS编程对货币供给与股票价格进行格兰杰因果关系检验,运用脉冲响应函数和方差分解对此因果关系及VAR模型的参数进行解释后,得出的结论却与理论分析有所不同：股票价格变化对货币供给变化具有显著的影响,且对各层次货币供给影响程度不同,但货币供给变化对股票价格变化的影响却不显著。因此,管理当局不仅要分析宏观经济对货币的需求,也要关注股票价格变化对货币供给的影响。     

Tracing ‘driver’ versus ‘modulator’ information flow throughout large-scale,task-related neural circuitry

Primary objective: To determine the relative uses of neural action potential (‘spike’) data versus local field potentials (LFPs) for modeling information flow through complex brain networks. Hypothesis: The common use of LFP data, which are continuous and therefore more mathematically suited for spectral information-flow modeling techniques such as Granger causality analysis, can lead to spurious inferences about whether a given brain area ‘drives’ the spiking in a downstream area. Experiment: We recorded spikes and LFPs from the forelimb motor cortex (M1) and the magnocellular red nucleus (mRN), which receives axon collaterals from M1 projection cells onto its distal dendrites, but not onto its perisomatic regions, as rats performed a skilled reaching task. Results and implications: As predicted, Granger causality analysis on the LFPs—which are mainly composed of vector-summed dendritic currents—produced results that if conventionally interpreted would suggest that the M1 cells drove spike firing in the mRN, whereas analyses of spiking in the two recorded regions revealed no significant correlations. These results suggest that mathematical models of information flow should treat the sampled dendritic activity as more likely to reflect intrinsic dendritic and input-related processing in neural networks, whereas spikes are more likely to provide information about the output of neural network processing.     

世界股票市场统一指数设计与应用研究——对亚、美、欧三地区股票价格指数为样本的分析

文章以全球75个国家的股票市场指数为研究样本,根据指数的编制原则,采用派许指数和简单平均的计算方法,提出变化率的指数设计方法,分别设计出亚太地区、美洲地区、欧洲地区的股票价格指数,并以这三个地区为样本,编制出世界股票市场统一指数。最后实证检验了世界股票指数与各地区主要股票指数的相关性,运用Granger因果检验世界股价指数与世界GDP的因果关系,结果表明“世界股市是世界国民经济的‘晴雨表’”的结论。      

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.