中国国防费时间序列预测模型的建立

时间序列模型(ARMA)是一种精度较高的短期预测模型.本文综合运用B-J时间序列建模方法,对中国国防费时间序列平稳性进行了判别;利用单位根方法检验了时间序列的单整阶数;利用自相关函数和偏自相关函数判别了时间序列模型的自回归阶数(AR(p))和移动平均阶数(MA(q));最后利用Eviews统计软件建立了合适的中国国防费时间序列模型,并进行了分析和预测.     

中国股票市场的ARCH效应研究

一、引言 金融时间序列往往具有时变方差的特征，即在某些时期的波动十分剧烈，而另一些时期的波动又相对平稳。为了刻画时间序列的这一特征， Ｅｎｇｌｅ于１９８２年提出了自回归条件异方差（ＡＲＣＨ）模型，Ｂｏｌｌｅｒｓｌｅｖ又于１９８６年进一步提出了广义自回归条件异方差（ＧＡＲＣＨ）模型。ＧＡＲＣＨ模型的本质特征是随机误差项的条件方差服从ＡＲＭＡ过程。设｛ｙｔ｝为一定平稳时间序列，ＧＡＲＣＨ（ｐ，ｑ）模型可表述为： 其中，Φ（Ｌ）为滞后算子Ｌ的多项式，特征方程Φ（２）＝０的根全位于单位圆外。ｖｔ为方差等于 …     

自回归滑动平均模型中阶数及参数的确定

时间序列中的模型,即自回归滑动平均模型,是在20世纪70年代由Box-Jenkins提出的,由于其准确的短期预测能力,近年来在经济预测方面得到了广泛应用.建立模型的关键在于模型阶数及参数的确定,过去许多文献探讨过有关定阶和参数估计的方法,但其中无论是AIC、BIC准则法还是观察自相关函数图和偏自相关函数图法等,往往对两个阶数相近的模型不能作有效的优劣判断,建模不够准确且效率不高.为了更准确、高效的建立模型,本文提出在估计法下通过LM检定、t检定及Q检定建立模型的方法,并通过对1952～2002年陕西省GDP水平的实证研究验证了该方法的可行性.     

论回归分析中的两类误差

一、问题的提出回归分析是利用回归方程（函数）研究总体变量间依存关系的一种专门方法。由于研究总体的大量性所决定，实践中，回归分析一般是依据样本资料进行的，是一个以样本观察的数量关系──样本回归函数（SRF，或称经骏函数）去推断总体真实的数量关系──总体回归函数（PRF）的抽样推断过程。以一元线性回归为例，设：总体回归模型（2）（X1，Y1），（X2，Y2）……（Xn，Yn）为取自总体的一个样本，拟合得：SRF：（3）回归分析的过程实质上就是用（3）式估计（2）式，从而对总体进行经济预测、结构分析、政策评价等。显然，…     

基于季节性ARIMA模型的居民消费水平预测

文章以居民消费价格指数(CPI)的短期预测作为切入点,采用定量的时间序列分析方法,建立季节自回归综合移动平均(季节性ARIMA模型)模型对CPI时间序列进行量化分析.首先阐述基于该模型的CPI预测的一般过程,即:平稳化处理、差分变换的阶数辨识、参数估计,时间序列模型的构建,然后对模型进行性能检验,确定较适合的季节自回归综合移动平均模型,最后在实证分析中探讨经济变量CPI与时间变量之间的变动规律,对CPI时间序列进行适当的差分处理,取得了较为理想的预测效果.     

基于质量损失函数的ARMA控制图经济设计

为提高统计过程监控灵敏度并减少监控费用,文章研究了自回归移动平均(ARMA)过程的统计与经济设计问题.首先构建ARMA控制图监控自相关过程,基于田口质量损失函数对ARMA控制图进行经济设计并构建经济模型;利用遗传算法求解,确定ARMA控制图的五个参数[n,h,k,φ,θ]最优组合,使单位时间费用最低.最后,对该模型的参数进行灵敏度分析,由此得出在实际监控过程中与单位时间期望费用相关的参数,以减少质量损失.     

基于EWMA组合模型的人民币汇率的动态预测

文章提出指数加权移动平均(EWMA)组合模型克服了传统组合方法没有考虑时序数据间时隔远近而相互影响不同的动态关联缺陷.对汇改后人民币汇率实证分析,结果发现EWMA组合模型比被组合的广义自回归条件异方差(GARCH)模型和均值回复(Mean Reversion)模型有更好的预测精度,能够更加逼真把握金融时序的未来走势.     

ESTAR模型单位根检验的性质研究

文章以转移函数为指数形式的平滑转移自回归模型(ESTAR)作为典型代表,通过模拟实验考察了非线性单位根检验(包括ADF检验和PP检验)的小样本性质;以MA(1)和GARCH(1,1)过程为代表,研究了误差项服从序列相关和异方差时,非线性参数和滞后阶数对两类检验统计量实际水平和功效的影响,从而为非平稳STAR族模型的应用研究提供一定的理论支持.     

基于自回归移动平均及支持向量机的中国棉花价格预测

文章通过对2008年至2011年间月度棉花价格数据进行分析,建立了基于自回归移动平均的棉花价格ARIMA(1,1,1)模型,结果显示,ARIMA(1,1,1)模型能够很好的模拟国内棉花价格,平均相对误差百分比低于4％,在ARIMA模型的基础上,对该模型残差建立支持向量机模型,将自回归移动平均模型与SVM模型组合对棉花价格进行了预测,比较预测结果,组合预测模型对自回归移动平均模型有一定改进.     

自相关过程的ARMA控制图

由于自相关过程违背了过程输出数据独立性的假定,使得常规控制图的有效性降低。ARMA控制图技术使用自回归移动平均模型作为统计量,依据两个信噪比指标来确定适宜的参数值。文章运用平均链长(ARL)系统研究了ARMA控制图的检测性能,并与残差图进行了比较。模拟结果表明,在自相关条件下,ARMA控制图对均值偏移具有较高的检测灵敏性。     

基于一种贴近度的IOWA算子的优性组合预测模型

传统的组合预测模型中每一种单项预测方法在各个时点具有相同的加权系数,但实际上同一种单项预测方法在各个时点的预测精度有高有低,为了克服单项预测方法取固定权系数的缺陷,构建了基于一种贴近度的IOWA算子的变权系数的组合预测模型,并探讨模型的非劣性组合预测、优性组合预测存在性的充分条件,实例分析结果表明：该模型在预测效果评价指标体系中明显优于传统的组合预测方法。     

Spatial variability clustering for spatially dependent functional data

This paper introduces a method for clustering spatially dependent functional data. The idea is to consider the contribution of each curve to the spatial variability. Thus, we define a spatial dispersion function associated to each curve and perform a k-means like clustering algorithm. The algorithm is based on the optimization of a fitting criterion between the spatial dispersion functions associated to each curve and the representative of the clusters. The performance of the proposed method is illustrated by an application on real data and a simulation study.     

多边价格指数中的价格异方差问题与CPD法的改进

CPD法是国际比较项目（ICP）中应用的一种重要的多边价格比较方法,多边价格比较方法的可靠性直接影响ICP公布的各国购买力平价数据的可信度。本文从理论上系统地分析了CPD法中存在的价格异方差问题,引入一种消除异方差的WLS估计方法。然后以2011年ICP居民实际消费支出各大类商品的价格数据为例,实证说明WLS估计的CPD法能显著提高各国购买力平价数据的可靠性;相对而言,存在异方差的CPD法会系统性高估发展中国家的居民实际消费支出水平。     

Algorithms for frequency distributions: efficiency and generality comparisons

Five methods for forming empirical frequency distributions are outlined. A specific implementation of each is described, and theoretical comparison of their speed and storage is supplemented by simulation data to give a series of recommendations about the appropriateness of each for different situations. The index method is the fastest of those considered, but often uses excessive space. A method based on height-balanced trees is economical of space, and still has good speed. A method based on Quicksort is faster than the tree method, but uses more space.     

区域城市化水平的可变模糊识别评价方法研究

以可变模糊集理论为基础,提出一种新的区域城市化水平评价方法-可变模糊识别评价方法,详细论述了其建模过程。该方法能够科学、合理确定样本指标对各级指标标准区间的相对隶属度、相对隶属函数。并且能够通过变换模型及其参数,合理的给出样本的评价等级,评价结果的可信度较高。最后将该方法应用于江苏省城市化水平评价。结果表明,该方法计算简便,计算结果可靠,可以推广到我国其他地区的城市化水平评价和实现同一区域城市化进程的动态评价。     

基于犹豫概率模糊语言集的改进MULTIMOORA决策方法

为解决犹豫环境中由随机性和模糊性引起的不确定性对实际决策造成偏差的多准则群决策问题,文章提出一种基于犹豫概率模糊语言的改进MULTIMOORA决策方法。首先构建各决策者的犹豫概率模糊语言决策矩阵,利用熵值法与离差最大化和距离最小化法得到决策者权重;利用犹豫概率模糊加权平均算子将各决策者的决策矩阵聚合为综合矩阵,进而通过改进的MULTIMOORA方法得到最终排序结果;最后通过物流园区合作商选择的算例分析验证该算法的可行性和有效性,并与TOPSIS法、VIKOR法及HFL-MULTIMOORA进行对比分析。     

Fitting finite mixture models using iterative Monte Carlo classification

Parameters of a finite mixture model are often estimated by the expectation–maximization (EM) algorithm where the observed data log-likelihood function is maximized. This paper proposes an alternative approach for fitting finite mixture models. Our method, called the iterative Monte Carlo classification (IMCC), is also an iterative fitting procedure. Within each iteration, it first estimates the membership probabilities for each data point, namely the conditional probability of a data point belonging to a particular mixing component given that the data point value is obtained, it then classifies each data point into a component distribution using the estimated conditional probabilities and the Monte Carlo method. It finally updates the parameters of each component distribution based on the classified data. Simulation studies were conducted to compare IMCC with some other algorithms for fitting mixture normal, and mixture t, densities.     

Regression analysis of competing risks data via semi-parametric additive hazard model

When the subjects in a study possess different demographic and disease characteristics and are exposed to more than one types of failure, a practical problem is to assess the covariate effects on each type of failure as well as on all-cause failure. The most widely used method is to employ the Cox models on each cause-specific hazard and the all-cause hazard. It has been pointed out that this method causes the problem of internal inconsistency. To solve such a problem, the additive hazard models have been advocated. In this paper, we model each cause-specific hazard with the additive hazard model that includes both constant and time-varying covariate effects. We illustrate that the covariate effect on all-cause failure can be estimated by the sum of the effects on all competing risks. Using data from a longitudinal study on breast cancer patients, we show that the proposed method gives simple interpretation of the final results, when the primary covariate effect is constant in the additive manner on each cause-specific hazard. Based on the given additive models on the cause-specific hazards, we derive the inferences for the adjusted survival and cumulative incidence functions.     

人口普查覆盖误差估计方法综述

人口普查工作的质量主要体现在覆盖误差的规模上.人口统计学家创建了估计人口普查覆盖误差的方法.有些方法利用独立于人口普查本身的信息,另外一些方法则利用人口行政记录的信息.由于每种方法都有其特定的形成背景和适用范围,因而没有适合于所有国家和地区的通用方法.通过对美国、新西兰、澳大利亚、英国和中国的人口普查覆盖误差估计方法进行了较为详细的介绍,说明了这些方法的使用情况.研究表明,任何一种估计方法都有其局限性,需要不断改进与完善.     

THE ANALYSIS OF DISCRETE CHOICE EXPERIMENTS WITH CORRELATED ERROR STRUCTURE

In a stated preference discrete choice experiment each subject is typically presented with several choice sets, and each choice set contains a number of alternatives. The alternatives are defined in terms of their name (brand) and their attributes at specified levels. The task for the subject is to choose from each choice set the alternative with highest utility for them. The multinomial is an appropriate distribution for the responses to each choice set since each subject chooses one alternative, and the multinomial logit is a common model. If the responses to the several choice sets are independent, the likelihood function is simply the product of multinomials. The most common and generally preferred method of estimating the parameters of the model is maximum likelihood (that is, selecting as estimates those values that maximize the likelihood function). If the assumption of within-subject independence to successive choice tasks is violated (it is almost surely violated), the likelihood function is incorrect and maximum likelihood estimation is inappropriate. The most serious errors involve the estimation of the variance-covariance matrix of the model parameter estimates, and the corresponding variances of market shares and changes in market shares.

In this paper we present an alternative method of estimation of the model parameter coefficients that incorporates a first-order within-subject covariance structure. The method involves the familiar log-odds transformation and application of the multivariate delta method. Estimation of the model coefficients after the transformation is a straightforward generalized least squares regression, and the corresponding improved estimate of the variance-covariance matrix is in closed form. Estimates of market share (and change in market share) follow from a second application of the multivariate delta method. The method and comparison with maximum likelihood estimation are illustrated with several simulated and actual data examples.

Advantages of the proposed method are: 1) it incorporates the within-subject covariance structure; 2) it is completely data driven; 3) it requires no additional model assumptions; 4) assuming asymptotic normality, it provides a simple procedure for computing confidence regions on market shares and changes in market shares; and 5) it produces results that are asymptotically equivalent to those produced by maximum likelihood when the data are independent.