The Comparison of Performances of Widely Used Cointegration Tests

The aim of this study is to compare performances of commonly cointegration tests used in literature in terms of their empirical power and type I error probabilty for various sample sizes. As a result of the study, it has been found that some tests are not appropriate in testing cointegration in terms of empirical power and type I error probability. As a result of simulation study, λ_max test for any values of ρ and sample sizes have been found most appropriate test in conclusion.     

Asymmetric Forecast Densities for U.S. Macroeconomic Variables from a Gaussian Copula Model of Cross-Sectional and Serial Dependence

Most existing reduced-form macroeconomic multivariate time series models employ elliptical disturbances, so that the forecast densities produced are symmetric. In this article, we use a copula model with asymmetric margins to produce forecast densities with the scope for severe departures from symmetry. Empirical and skew t distributions are employed for the margins, and a high-dimensional Gaussian copula is used to jointly capture cross-sectional and (multivariate) serial dependence. The copula parameter matrix is given by the correlation matrix of a latent stationary and Markov vector autoregression (VAR). We show that the likelihood can be evaluated efficiently using the unique partial correlations, and estimate the copula using Bayesian methods. We examine the forecasting performance of the model for four U.S. macroeconomic variables between 1975:Q1 and 2011:Q2 using quarterly real-time data. We find that the point and density forecasts from the copula model are competitive with those from a Bayesian VAR. During the recent recession the forecast densities exhibit substantial asymmetry, avoiding some of the pitfalls of the symmetric forecast densities from the Bayesian VAR. We show that the asymmetries in the predictive distributions of GDP growth and inflation are similar to those found in the probabilistic forecasts from the Survey of Professional Forecasters. Last, we find that unlike the linear VAR model, our fitted Gaussian copula models exhibit nonlinear dependencies between some macroeconomic variables. This article has online supplementary material.     

基于EMD与DVG的非线性时间序列预测模型及其应用研究

针对具有非线性和不稳定性的时间序列,提出一种结合经验模态分解(EMD)、有向可见图(DVG)网络的动态预测模型。利用经验模态分解将原时间序列分解为多个固有模态函数(IMF),然后对分解后的高频和低频IMF利用快速傅里叶变换得到各自的周期;依据每个周期,从原时间序列的尾部截取长短不一的子序列,然后采用有向可见图算法转换为多个有向网络,利用随机游走在每个有向网络中寻找与时间序列最后一个节点相似的节点;最后,依据平行线法,预测时间序列的下一个数值。原油价格的时间序列是一类典型的具有非线性和不稳定性的序列,利用此模型对WTI原油每日价格进行实证分析。研究结果表明,此模型不但可以有效地预测时间序列的变化趋势,而且具有较高的预测精度。     

三阶循环级数

循环级数是组合论中的重要内容之一,是计数和作算法分析的基础,它几乎对一切数学分支都有着重要作用。在生产和实践中也有着广泛的应用。给出了三阶循环级数及推广的通项的一般求法,为研究一般递归关系提供了理论上的解决方法。     

A semiparametric approach for modelling multivariate nonlinear time series

In this article, a semiparametric time‐varying nonlinear vector autoregressive (NVAR) model is proposed to model nonlinear vector time series data. We consider a combination of parametric and nonparametric estimation approaches to estimate the NVAR function for both independent and dependent errors. We use the multivariate Taylor series expansion of the link function up to the second order which has a parametric framework as a representation of the nonlinear vector regression function. After the unknown parameters are estimated by the maximum likelihood estimation procedure, the obtained NVAR function is adjusted by a nonparametric diagonal matrix, where the proposed adjusted matrix is estimated by the nonparametric kernel estimator. The asymptotic consistency properties of the proposed estimators are established. Simulation studies are conducted to evaluate the performance of the proposed semiparametric method. A real data example on short‐run interest rates and long‐run interest rates of United States Treasury securities is analyzed to demonstrate the application of the proposed approach. The Canadian Journal of Statistics 47: 668–687; 2019 © 2019 Statistical Society of Canada     

FEPA-金融时间序列自适应组合预测模型

本文报告一种金融时间序列预测的信号分析、信息融合与智能计算组合模型,简称FEPA,由针对金融时间序列（FTS）信号分析的经验模态分解（EMD）、用于数据降维的主成分分析（PCA）和用于非线性建模的人工神经网络（ANN）三部分组成。该模型首先应用滑动窗口截取原始金融时间序列最近期数据集,应用EMD分解算法把数据集分解成不同尺度的本征模态函数（IMF）,然后通过主成分分析将分解后的数据降维,提取最有信息量的特征;然后将这些特征输入到神经网络进行组合预测。本文提出的组合预测模型FEPA是基于分解-提优-合成的信息融合思想,有效提高了预测可靠性。其创新点在于：1）首次给出了EMD算法的结构化表达,提供了今后融合更多信息的算法接口;2）通过多步长预测输出深入研究EMD分解的有效信息结构;3）通过切换到更细时间框架来处理EMD的端点效应,并探索了两级时间框架下的预测效果;4）给出了金融时间序列组合预测模型的一般性架构,具有可升级性和可扩展性。并且通过滑动窗口EMD使得实证更能切近实际。通过在沪深300股指和澳大利亚股指上的实证,结果表明FEPA预测模型在沪深300股指日线和15分钟线上的预测命中率高达78%和82%,在澳大利亚股指日线上也达到了74%的命中率,经比较,明显高于文献中常见的5种模型。     

高校实施ISO9000标准的若干问题探讨

ISO9000族标准是国际标准化组织颁布的世界通用的质量管理标准。目前,国内一些高等学校开始按照ISO9000标准建立质量管理体系。电子科技大学中山学院在这种背景下,在全院范围内推行ISO9000质量管理体系。在这一过程中,产生了一些值得探讨的问题,出现了一些实际操作的难点,现提出来与大家共同探讨。     

福建经济若干统计特征及发展趋势

选取福建省．1983年以来经济部分指标进行了统计分析。描述了一些指标的变化规律,划分了经济发展的阶段,采用新方法预测了2006年和2007年的经济指标,进而得出若干启示和分析结论。