Towards a taxonomy of forecast error measures a factor-comparative investigation of forecast error dimensions

This study explores the nature of information conveyed by 14 error measures drawn from the literature, using real-life forecasting data from 691 individual product items over six quarterly periods. Principal components analysis is used to derive factor solutions that are subsequently compared for two forecasting methods, a version of Holt's exponential smoothing, and the random walk model (Naive 1). The results reveal four underlying forecast error dimensions that are stable across the two factor solutions. The potentially confounding influence of sales volume on the derived error dimensions is also explored via correlation analysis.     

EB RULES FOR SELECTING THE BEST EXPONENTIAL POPULATION

In this paper,we obtain empirical Bayes(EB)procedures for selecting the bestamong k different exponential populations(the form of the conditional probability densitye.g.each population is f_4(x_i/b_i)=b_i exp(-b_ix_i),x_i,b_i∈(0,∞),i=1,2,…,k).These rulesare based on the EB estimators of b_i.We show that,under the squared error loss,the Bayesrisk of the EB estimators converges to the related minimum Bayes risks with rates of conver-gence at Jeast of order O(n~(-q)).Further,for the selection problem,the rates of convergenceof the proposed selection rules are shown to be at least of order O(n~((-q)/2) where q can bearbitrarily close to 1/5 or 1 under suitable conditions.     

Judgemental revision of sales forecasts: Effectiveness of forecast selection

When managers make revisions to sales forecasts initially generated by a rational quantitative model it is important that the particular forecasts selected for adjustment are those which would benefit most from the adjustment process (i.e. realize high errors). This study reports an empirical investigation on this issue, spanning six quarterly forecasting periods and incorporating forecasting data on over 850 products. The results show that the errors of the forecasts chosen for revision are, in general, higher than those which were not chosen. In addition, it is shown that managesrs tend to revise forecasts which are initially low, hence possibily introducing some degree of bias into the overall forecasts.     

Bootstrapping forecast intervals: An application to AR(p) models

Forecast intervals typically depend upon an assumption of normal forecast errors due to lack of information concerning the distribution of the forecast. This article applies the bootstrap to the problem of estimating forecast intervals for an AR(p) model. Box-Jenkins intervals are compared to intervals produced from a naive bootstrap and a bias-correction bootstrap. Substantial differences between the three methods are found. Bootstrapping an AR(p) model requires use of the backward residuals which typically are not i.i.d. and hence inappropriate for bootstrap resampling. A recently developed method of obtaining i.i.d. backward residuals is employed and was found to affect the bootstrap prediction intervals.     

经验数字特征的收敛性

在文[3]的基础上，进一步给出了随机变量x的k阶经验原点矩Ex^k及二阶经验中心矩的定义，并证明了k阶经验原点矩及二阶经验中心矩的收敛性，讨论了k阶经验原点矩与经验特征函数的关系,为母体参数的估计提供了一个新的途径，     

Algorithmic randomness in empirical data

According to a traditional view, scientific laws and theories constitute algorithmic compressions of empirical data sets collected from observations and measurements. This article defends the thesis that, to the contrary, empirical data sets are algorithmically incompressible. The reason is that individual data points are determined partly by perturbations, or causal factors that cannot be reduced to any pattern. If empirical data sets are incompressible, then they exhibit maximal algorithmic complexity, maximal entropy and zero redundancy. They are therefore maximally efficient carriers of information about the world. Since, on algorithmic information theory, a string is algorithmically random just if it is incompressible, the thesis entails that empirical data sets consist of algorithmically random strings of digits. Rather than constituting compressions of empirical data, scientific laws and theories pick out patterns that data sets exhibit with a certain noise.     

Beta定价理论与实证研究

资本资产定价问题是投资学的核心问题，我们考察Beta定价理论的实证方法，并对随机选取的20家上市公司(沪、深各10家)的Beta定价有效性做了讨论，从统计结果来看，虽然单个证券的收益率ri^(t)与证券组合收益率R^(t)之间呈现出一定的线性关系，且残差序列呈现序列无关性，但JB检验的结果却表明残差序列并不服从正态分布，R2统计值偏小，说明β对市场的解释力度明显不足，从而说明Beta定价理论不能完全解释中国证券市场的价格行为，同时也说明资本资产定价模型不完全适合中国证券市场的市场行为。     

法治框架下转基因食品认知的实证展

法治是一种规则之治.当代中国语境下的转基因食品面临技术与经验的博弈.法治框架下的转基因食品认知不仅涉及民众切身利益,且关乎整个国家经济、社会繁荣与发展.从法治视角审视当下中国对于转基因食品的诸多争议,有助于更好地把握现代科学技术在法治进程中所扮演的多重角色与身份.基于此,法治框架下的转基因食品认知可以且应当以实证调查与分析为切入点,在技术与经验之间寻找符合法治规则的平衡点.     

宁波FDI技术溢出效应的实证分析

FDI的技术溢出效应引起了理论界广泛关注,但现有研究认为FDI对不同地区的作用不一定相同。文章以宁波为例,根据柯布-道格拉斯生产函数建立衡量技术溢出效应的数学模型,利用宁波市相关数据,对其FDI技术溢出效应进行了实证分析。研究结果表明：宁波外商直接投资对本地经济产生了负面技术外溢效应,其原因在于宁波企业主要以劳动密集型为主,技术水平较低,与跨国公司差距大,自身技术吸收能力低。为此,文章从调整引资结构、促进内外资企业联动发展等方面提出了提升宁波FDI技术溢出效应的若干建议。     

财务重述与公司治理最佳效率点的关系

采用实证研究方法,以我国深、沪市A股市场在2008年-2011年间的364份财务重述公司和4904份非财务重述公司为研究样本,研究发现董事会会议次数、高管薪酬及是否由四大会计师事务所审计显著影响公司治理效率;并采用因子分析的方法建立公司治理指数(G),发现与财务重述的发生呈U型关系,证明了公司治理结构最佳效率点的存在;与此同时,公司所处的发展阶段和代理成本均显著影响公司治理效率。