我国正规就业者的教育收益率

针对教育收益率测算中可能存在的弱工具变量问题,本文利用2006年中国健康与营养调查数据,结合工具变量估计框架下的各种模型设定检验,对我国正规就业者的教育收益率进行测算。检验和测算结果表明：受教育程度的变量存在内生性,个体配偶的受教育年限是内生变量受教育程度的强工具变量,而个体的出生季度是弱工具变量。广义矩估计结果显示我国正规就业者的教育收益率为10.1%。     

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。     

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。      

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。      

Testing the equality of two independent regression models

In some situations, for example in agriculture, biology, hydrology, and psychology, researchers wish to determine whether the relationship between response variable and predictor variables differs in two populations. In other words, we are interested in comparing two regression models for two independent datasets. In this work, we will use the parametric and nonparametric methods to establish hypothesis testing for the equality of two independent regression models. Then the simulation study is provided to investigate the performance of the proposed method.     

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.     

Estimating the error distribution function in semiparametric additive regression models

We consider semiparametric additive regression models with a linear parametric part and a nonparametric part, both involving multivariate covariates. For the nonparametric part we assume two models. In the first, the regression function is unspecified and smooth; in the second, the regression function is additive with smooth components. Depending on the model, the regression curve is estimated by suitable least squares methods. The resulting residual-based empirical distribution function is shown to differ from the error-based empirical distribution function by an additive expression, up to a uniformly negligible remainder term. This result implies a functional central limit theorem for the residual-based empirical distribution function. It is used to test for normal errors.