基于网购搜索量的CPI及时预测模型

购物网站的用户搜索量数据是近年来出现的一种新类型数据源。基于该数据在合理选择关键词以及对数据进行季节调整和假日处理的基础上建立中国全国与城镇CPI的及时预测模型。模型以分布滞后模型为基础,采用Elastic-Net方法进行收缩估计进而实现变量选择。在确定最优惩罚因子和调整参数时采用了K重交叉验证技术。实证结果表明,搜索量变量与CPI具有显著的因果关系,在此基础上建立的预测模型经济含义合理,并能对CPI做出较精确的预测。同时,从模型均方误差角度看,基于Elastic-Net的变量选择显著优于基于逐步回归的方法,而城镇CPI预测模型也优于全国CPI预测模型。     

引入营销效应的文本情感挖掘研究

大量的在线评论文本数据,对汽车制造业的产品创新、精准营销具有重要的实用价值.传统的文本挖掘多关注情感极性强度而忽视了情感营销效应,挖掘结果与企业需求差异较大.文章利用大连理工、中国知网等情感词典和丰富的汽车语料库,采取了基于CRF机器学习的情感词自动获取方法,构建了情感营销效应词库,并运用MATLAB对该词库情感营销效应进行了全面的统计分析,分类优化情感营销效应值,最后采用中科院ICTCLAS中文分词技术进行测试,结果表明,考虑情感营销效应的文本挖掘召回率达到80％以上,能较好地满足汽车厂商的应用需求.     

基于“21世纪海上丝绸之路”文献的文本挖掘研究

对以"21世纪海上丝绸之路"为主题的3 399篇科技文献,采用关联分析、TF-IDF词频分析、LDA模型等文本挖掘手段及其可视化方法,分别针对科技文献的关键词、摘要、研究机构、发表时间、内容等信息进行文本建模与分析,从而形成全新视角下文本数据的定量研究。研究发现:对该问题的研究具有显著的区域性特征和地理溢出效应;当前研究侧重宏观战略角度,缺乏技术层面的量化研究;时间序列分析表明宏观战略与新闻类文献数量趋缓,为后续各子命题的定量分析提供了一定的空间。     

一种基于网络爬虫技术的价格指数计算模型

本文参照官方CPI的制度方法,设计了一种基于网络爬虫技术的价格指数计算模型.通过模型试算值与官方数据的比较,以及对原始数据的特征挖掘,发现该种模型具有时效性强和灵敏度高的特点.     

两次金融危机影响下中国CPI运行对比分析

研究发现1997年金融危机发生前后CPI运行相关度非常高,而2008年金融危机发生当期CPI与2007CPI运行相关度非常高,并且对2008、2009年前5个月同比CPI数据进行分析,相关度也非常高。对此,文章在此基础上运用BP神经网络模型具有很好地模拟非线性系统的优点,对BP神经网络模型进行了改进,并运用该模型对中国2001～2008年前5个月月度CPI指数进行了拟合的基础上,对2009年后7个月月度CPI指数和全年CPI指数进行了预测。将2009年全年数据(包括已公布的前5个月数据和后7个月的预测数)与2008年数据进行相关分析,发现相关度非常高。     

基于月环比均值和翘尾因素的居民消费价格指数短期预测

文章利用居民消费价格指数(CPI)月环比所具有的月度特点和春节特点,通过根据月份选用不同样本空间计算月环比的方法,建立了预测CPI的新模型。通过试预测,文章认为此模型具有较好的预测精度,并可根据已发生的CPI月环比真实值及时对预测结果进行修正,以提高预测的准确性。     

基于季节增长率的一种直接调整方法

传统季节调整方法在提取环比增长率时需要先剔除原始数据中的季节成分,这会带来原始数据信息的失真.鉴于此,本文提出了一种直接拟合原始数据增长率的季节增长率(SGR)模型,该模型不仅可以直接提取环比增长率,还可以对原始数据的增长率进行预测.蒙特卡洛模拟结果表明,本文给出的针对SGR模型的MLE估计方法具有良好的有限样本表现.通过对我国GDP和CPI数据进行实证,本文发现利用SGR模型直接提取的环比增长率的稳定性要高于其他一些季节调整方法.不仅如此,SGR模型的拟合和预测表现相比BSM模型和SARIMA模型均有显著提高.此外,SGR模型还具有容易拓展为非线性、多元情形的优势.     

CPI月度环比指数季节调整及CPI折年率方法研究

内容提要：CPI中长期变动趋势的预测一直是困扰经济学界的一个问题。本文研究了CPI月度环比指数的季节调整,并建立了CPI中长期预测的模型,并利用该模型对我国今年CPI上涨趋势进行了预测。     

我国居民消费价格波动和预测：1997-2010

 CPI与人们的生活息息相关,同时也是经济分析和决策、价格监测和调控及国民经济核算的重要指标。本文以1997年1月至2009年12月我国衣食住行及城乡分类月度定基CPI数据为样本,在分析其波动特征及差异的基础上,通过指数平滑法的Holt-Winters模型将其分解为季节和趋势波动。结果表明,我国分类CPI各自具有明显的趋势和季节特征,并得出其波峰和波谷到达时间;模型对其有非常好的拟合效果,其MAPE依次为0.253%、0.816%、0.364%、0.359%、0.391%、0.338%。在此基础上对我国2010年各月的分类CPI进行了科学预测。     

提高CPI数据质量的编制技术研究评述

提高CPI数据质量既需要采集的调查数据准确可靠,也需要编制指数的技术方法科学合理。消费者价格指数（CPI）通常是根据标准的指数方法计算而得,本文主要从指数编制技术的角度出发,分别从CPI的构造方法、质量调整、季节性产品的处理三个方面,对提高CPI数据质量的编制技术进行研究评述。在此基础上,本文从编制技术的角度,对提高我国CPI数据质量提出相应建议与对策。     

《统计研究》的历史阶段性回顾与特征分析

本文基于各个时代背景下我国统计学科的重要事件,联系过去十四届中青年统计科学研讨会的会议主题,利用文本数据挖掘技术,对1984年至2014年第5期《统计研究》刊登的5192篇文章的篇名、关键词和摘要等做深入分析。探讨了我国30年来统计学科的发展历程,剖析了《统计研究》期刊的整体特征,进一步明确了《统计研究》杂志对中国统计学科建设发展的重要贡献。     

A method for K-Means seeds generation applied to text mining

In this paper, a methodology is proposed in order to produce a set of seeds later used as a starting point to K-Means-type unsupervised classification algorithms for text mining. Our proposal involves using the eigenvectors obtained from principal component analysis to extract initial seeds, upon appropriate treatment for search of lightly overlapping clusters which are also clearly identified by keywords. This work is motivated by the interest of the authors in the problem of identification of topics and themes previously unknown in short texts. Therefore, in order to validate the goodness of this method, it was applied on a sample of labeled e-mails (NG20) representing a gold standard within the field of text mining. Specifically, some corpora referenced in the literature have been used, configured in accordance to a mix of topics contained in the sample. The proposed method improves on the results of other state-of-the-art methods to which it is compared.     

Nonlinear mixed-effects scalar-on-function models and variable selection

This paper is motivated by our collaborative research and the aim is to model clinical assessments of upper limb function after stroke using 3D-position and 4D-orientation movement data. We present a new nonlinear mixed-effects scalar-on-function regression model with a Gaussian process prior focusing on the variable selection from a large number of candidates including both scalar and function variables. A novel variable selection algorithm has been developed, namely functional least angle regression. As it is essential for this algorithm, we studied the representation of functional variables with different methods and the correlation between a scalar and a group of mixed scalar and functional variables. We also propose a new stopping rule for practical use. This algorithm is efficient and accurate for both variable selection and parameter estimation even when the number of functional variables is very large and the variables are correlated. And thus the prediction provided by the algorithm is accurate. Our comprehensive simulation study showed that the method is superior to other existing variable selection methods. When the algorithm was applied to the analysis of the movement data, the use of the nonlinear random-effect model and the function variables significantly improved the prediction accuracy for the clinical assessment.

     

全球通货膨胀的驱动因素与非对称冲击分析——基于扩展的新凯恩斯菲利普斯曲线的实证分析

将全球流动性、全球工资水平、美元汇率、国际资源品价格、全球经济增长等因素引入具有微观基础的新凯恩斯菲利普斯曲线模型中,利用状态空间模型实证分析了各变量对全球通货膨胀影响的变动状况,分析了美国和中国CPI的驱动因素。结果表明：全球流动性是全球通货膨胀产生的最主要驱动因素,其次是美元汇率、全球经济增长和国际资源品价格,三者具有持续且稳定的影响力。中国需求因素影响逐渐增强。全球工资水平对通货膨胀的影响不明显。美国CPI的重要推动因素是居民可支配收入,中国CPI的重要推动因素是经济增长率和国际资源品价格。     

Minimum,maximum, and average spherical prediction variances for central composite and box-behnken designs

Single value design optimality criteria are often considered when selecting a response surface design. An alternative to a single value criterion is to evaluate prediction variance properties throughout the experimental region and to graphically display the results in a variance dispersion graph (VDG) (Giovannitti-Jensen and Myers (1989)). Three properties of interest are the spherical average, maximum, and minimum prediction variances. Currently, a computer-intensive optimization algorithm is utilized to evaluate these prediction variance properties. It will be shown that the average, maximum, and minimum spherical prediction variances for central composite designs and Box-Behnken designs can be derived analytically. These three prediction variances can be expressed as functions of the radius and the design parameters. These functions provide exact spherical prediction variance values eliminating the implementation of extensive computing involving algorithms which do not guarantee convergence. This research is concerned with the theoretical development of these analytical forms. Results are presented for hyperspherical and hypercuboidal regions.     

An investigation of a quadratic design criterion for estimation of the hemodynamic response function

Optimal experimental design for estimation of the hemodynamic response function (HRF) is investigated using a nonlinear model with a quadratic mean squared error design criterion. This criterion is used, along with a genetic algorithm, to select locally optimal designs that are shown to be, in most cases, more efficient than designs selected with the more commonly used linear expansion criterion. These designs are also shown to result in lower overall asymptotic estimator variance and bias. The investigation focuses on a single stimulus type, but the criterion can also be used with multiple stimulus types.     

经济周期波动的函数型时序分解方法——基于CPI的实证分析

建立一个函数型时序分解模型,根据交叉验证方法将数据分为趋势项、周期项和随机项,因而提取出的趋势项具有较好的泛化能力;提出的基于调节粗惩系数的转折点选取法,通过优化粗惩系数较好地分割了CPI的扩张期和收缩期,可判断经济指数的转折点。另外利用傅里叶变换(FFT)提取数据主频,改进了周期型基函数,相比于传统的傅里叶基函数,新的周期基函数对周期项的拟合精度较高。通过对近十年和近两年的CPI数据进行分析,结果表明季节影响较为明显,而且最后的组合模型预测精度较高。     

Application of genetic algorithms to the construction of exact D-optimal designs

This paper studies the application of genetic algorithms to the construction of exact D-optimal experimental designs. The concept of genetic algorithms is introduced in the general context of the problem of finding optimal designs. The algorithm is then applied specifically to finding exact D-optimal designs for three different types of model. The performance of genetic algorithms is compared with that of the modified Fedorov algorithm in terms of computing time and relative efficiency. Finally, potential applications of genetic algorithms to other optimality criteria and to other types of model are discussed, along with some open problems for possible future research.