股票分类指数的多元马尔可夫链模型

针对多元马尔可夫链模型分析中,待估参数数量大而导致估计困难的问题,文章提出了改进方法。以一元预测误差最小为优化目标,对列间权重参数进行了分批次优化求解。应用多元马尔可夫链模型,对股票五大行业分类指数序列进行建模,研究了各行业分类指数相互间的内在相依特征。应用数据序列间的多元交互信息,对行业分类股指序列进行了预测。     

消费者食品安全安心度指数的编制

食品安全并不等同于消费者安心,分析消费者的食品安全安心度水平是政府判断食品安全监管绩效和制定食品安全风险交流政策的重要依据.文章构建了一个消费者食品安全安心度指数的评价体系,并利用消费者问卷调查数据,对消费者食品安全安心度指数进行了测算和分析.     

交易型开放式指数基金营销策略

相比于普通开放式基金更具有便利选择、收益稳定等多种优点的交易型开放式指数基金(Exchange Traded Fund,ETF),规模增长却相对有限。本文借助SWOT分析,明确其在中国市场的定位,并在全面阐释ETF的基础上,对其发展缓慢的原因进行了深入剖析,提出了有针对性的营销策略。     

国民经济核算体系中的指数编制研究

经济指数作为一种对复杂经济现象进行综合比较的工具,在国民经济核算体系研究中具有比较重要的地位。 首先,国民经济运行是静态联系与动态运动的统一。研究国民经济核算体系问题,不仅要研究其静态的核算结构如:国民收入核算表、资金流     

我国金属期货价格指数编制及其实证研究

 本文研究和总结了商品期货价格指数编制理论和方法,梳理了国内外商品期货价格指数编制的成功经验,并基于前述研究基础设计了五种我国金属期货价格指数编制方案。最后基于实证检验的方法,通过对比各方案指数在指数平稳性、指数投资功能、对宏观经济的反应等方面的表现,确定“持仓量”滚动型方案为最优方案。 本文主要创新点在于第一次系统、全面地研究并提出了商品期货价格指数的基本理论和方法,第一次全方位、多角度地研究了我国金属期货价格指数的编制方法和方案,为建构我国商品期货价格指数体系奠定了坚实的理论基础,提供了具有较高通用性的编制方案,同时,为我国商品投资提供了客观的基准指数,为指数衍生产品创新提供了有效标的。     

同比价格指数与环比价格指数辨析

内容提要：同比价格指数与环比价格指数间有着重大的区别,同比指数反映了年度间的价格变化,环比指数反映了月（季）度间的价格变化。实证检验表明,同比价格指数序列与环比价格指数序列有着不同的单整阶数,两者之间不能互相替代。在利用多个宏观经济变量构建月度（或季度）经济计量模型时,不仅要注意各序列的单整性,而且要注意各序列的一致性。     

基于季节调整的CPI统计环比指数研究

本文首先阐述了季节调整与统计环比指数的必要性,简要介绍了X-12-ARIMA与TRAMO/SEATS季节调整原理,然后运用X-12-ARIMA程序对中国1997年1月至2010年5月CPI月度数据进行季节调整,再运用TRAMO/SEATS方法解决季节调整程序中中国春节因素问题。接着由季节调整后的数据计算得到月环比CPI,对月环比CPI和同比增加率进行了比较,结果显示月环比CPI领先同比CPI。最后利用TRAMO/SEATS程序建立ARIMA模型（210）（011）进行了24个月的预测,预测结果显示,未来24个月内我国消费者物价指数温和上升,不会发生大的通货膨胀,但是存在一定的通胀压力。     

GDP指数随机化分析

指数是用于计算事物变动,特别是用于计算不可直接加总的现象之间综合变动情况的一种相对数,按照不同的划分标准,指数可以分为单指数、综合指数,或者质量指数、数量指数等。指数分析是统计学的一个重要的分析方法。一般的指数分析包括绝对数分析和相对数分析,相对数分析是分析综     

Statistical Analysis of the Hirsch Index

Abstract. The Hirsch index (commonly referred to as h‐index) is a bibliometric indicator which is widely recognized as effective for measuring the scientific production of a scholar since it summarizes size and impact of the research output. In a formal setting, the h‐index is actually an empirical functional of the distribution of the citation counts received by the scholar. Under this approach, the asymptotic theory for the empirical h‐index has been recently exploited when the citation counts follow a continuous distribution and, in particular, variance estimation has been considered for the Pareto‐type and the Weibull‐type distribution families. However, in bibliometric applications, citation counts display a distribution supported by the integers. Thus, we provide general properties for the empirical h‐index under the small‐ and large‐sample settings. In addition, we also introduce consistent non‐parametric variance estimation, which allows for the implementation of large‐sample set estimation for the theoretical h‐index.     

论综合指数和平均数指数

综合指数和平均数指数在由简单形式发展到加权形式的过程中,源于L氏指数和P氏指数中同度量因素（权数）的选择和时间的固定的不同,在其编制和计算中形成了L氏指数法和P氏指数法—由此,加权综合指数和加权平均数指数构成了当代编制与计算总指数中最重要最基本的两种形式。于是在编制质量指标指数和数量指标指数时,分别采用P氏指数法和L氏指数法。加权综合指数和加权平均数指数在二者的变量（或指数化因素或指数化指标）和同度量因素（权数）、分子分母资料的表现形式、对资料的要求和使用等三方面有不同之处,但后者是前者的变形。     

Convergence of Heavy-tailed Monte Carlo Markov Chain Algorithms

Abstract.  In this paper, we use recent results of Jarner & Roberts ( Ann. Appl. Probab., 12, 2002, 224) to show polynomial convergence rates of Monte Carlo Markov Chain algorithms with polynomial target distributions, in particular random-walk Metropolis algorithms, Langevin algorithms and independence samplers. We also use similar methodology to consider polynomial convergence of the Gibbs sampler on a constrained state space. The main result for the random-walk Metropolis algorithm is that heavy-tailed proposal distributions lead to higher rates of convergence and thus to qualitatively better algorithms as measured, for instance, by the existence of central limit theorems for higher moments. Thus, the paper gives for the first time a theoretical justification for the common belief that heavy-tailed proposal distributions improve convergence in the context of random-walk Metropolis algorithms. Similar results are shown to hold for Langevin algorithms and the independence sampler, while results for the mixing of Gibbs samplers on uniform distributions on constrained spaces are rather different in character.     

Analysis of Double Single Index Models

Motivated from problems in canonical correlation analysis, reduced rank regression and sufficient dimension reduction, we introduce a double dimension reduction model where a single index of the multivariate response is linked to the multivariate covariate through a single index of these covariates, hence the name double single index model. Because nonlinear association between two sets of multivariate variables can be arbitrarily complex and even intractable in general, we aim at seeking a principal one‐dimensional association structure where a response index is fully characterized by a single predictor index. The functional relation between the two single‐indices is left unspecified, allowing flexible exploration of any potential nonlinear association. We argue that such double single index association is meaningful and easy to interpret, and the rest of the multi‐dimensional dependence structure can be treated as nuisance in model estimation. We investigate the estimation and inference of both indices and the regression function, and derive the asymptotic properties of our procedure. We illustrate the numerical performance in finite samples and demonstrate the usefulness of the modelling and estimation procedure in a multi‐covariate multi‐response problem concerning concrete.     

Markov Chain Monte Carlo methods for estimating surgery duration

Developing prediction bounds for surgery duration is difficult due to the large number of distinct procedures. The variety of procedures at a multi-speciality surgery suite means that even with several years of historical data a large fraction of surgical cases will have little or no historical data for use in predicting case duration. Bayesian methods can be used to combine historical data with expert judgement to provide estimates to overcome this, but eliciting expert opinion for a probability distribution can be difficult. We combine expert judgement, expert classification of procedures by complexity category and historical data in a Markov Chain Monte Carlo model and test it against one year of actual surgery cases at a multi-speciality surgical suite.     

中国国家价值链：区域关联特征与增加值收益变化

本研究通过1997、2002、2007、2012年中国区域间投入产出表,从中间品关联、增加值关联、投入产出关联三方面的视角度考察了中国8大区域间的国家价值链（NVC）现状及其演变趋势,并测算了不同地区参与NVC的增加值收益变化。研究发现,无论是中间品关联,还是增加值关联,中国8大区域间的关联程度总体呈现上升趋势,当然,不同地区也呈现分化现象：西北、京津、中部地区的国内中间品关联指数更高一些,而东北与东南沿海地区的该指数更低一些;京津、西北地区的增加值后向关联指数较大,而中部地区与北部沿海地区的增加值前向关联指数较大。从投入产出关联视角看,中国8大区域的上游度指数与下游度指数在1997年时均超过2,而且1997-2012年间,更多地区的上、下游度指数呈现上升态势。当然,从区域的角度考察,中国国内价值链不具有明显的“微笑曲线”特征。从参与NVC的增加值获益率上看,则是内陆地区普遍高于沿海地区,但随着参与GVC程度的增加,各地区从参与NVC的增加值获益率有下降的趋势。     

A Discrete Variable Chain Graph for Applicants for Credit

A bank offering unsecured personal loans may be interested in several related outcome variables, including defaulting on the repayments, early repayment or failing to take up an offered loan. Current predictive models used by banks typically consider such variables individually. However, the fact that they are related to each other, and to many interrelated potential predictor variables, suggests that graphical models may provide an attractive alternative solution. We developed such a model for a data set of 15 variables measured on a set of 14 000 applications for unsecured personal loans. The resulting global model of behaviour enabled us to identify several previously unsuspected relationships of considerable interest to the bank. For example, we discovered important but obscure relationships between taking out insurance, prior delinquency with a credit card and delinquency with the loan.