中国宏观经济统计数据异常性和波动性特征的计量检验:1953～2001

对于宏观经济统计数据的异常性和波动性进行分析，已成为研究数据质量的最核心内容之一。本文从经济系统的角度运用随机方差扩大模型对我国36个宏观经济时间序列的数据质量进行了全面分析，发现了数据异常及波动的特点和规律。研究结论表明，大部分异常点的出现或多或少都是以聚集成堆的形式出现，它们之间有深刻的内在联系，异常点的出现大多与各种历史因素以及外部冲击有关；几乎所有的原始序列都有显著的偏度，过多的峰度也是明显的，因此它们被显著地拒绝认为服从正态分布；名义序列的特征在更大程度上受到异常点的影响。     

论物流客户关系网络管理

从物流客户关系网络管理的产生入手,对基于网络的物流客户细分和物流客户关系网络管理的关键技术网络数据挖掘在物流客户关系网络管理中作用及功能进行分析,在此基础上建构物流客户关系网络管理系统。     

构建基于数据挖掘技术的军事物流信息平台

着眼于军事物流发展趋势,针对来．采战争对军事物流提出的新要求,在剖析当前军事物流信息平台弊端的基础上,提出了基于DM技术的军事物流信息平台,并对该平台的优势进行了详细论述。     

Characteristics and implications of Chinese macroeconomic data revisions

The research examining macroeconomic data for developed economies suggests that an understanding of the nature of data revisions is important both for the production of accurate macroeconomic forecasts and for forecast evaluation. This paper focuses on Chinese data, for which there has been substantial debate about data quality for some time. The key finding in this paper is that, while it is true that the Chinese macroeconomic data revisions are not well-behaved, they are not very different from similarly-timed U.S. macroeconomic data revisions. The positive bias in Chinese real GDP revisions is a result of the fast-growing service sector, which is notably hard to measure in real time. A better understanding of the revisions process is particularly helpful for studies of the forecast errors from surveys of forecasters, where the choice of the vintage for outcomes may have an impact on the estimated forecast errors.     

Models of static and dynamic technical efficiency of municipal libraries in the Czech Republic

This article deals with the modelling of the static and dynamic technical efficiency under conditions of municipal libraries of municipalities with 1000–5000 inhabitants. The aim of this article is to determine the level of the technical efficiency and the factors that influence the results of modelling of the static and dynamic technical efficiency of 34 selected municipal libraries for the years of 2011 and 2015. The first model tests the technical efficiency of conventional services of public libraries. The second model tests the technical efficiency of municipal libraries’ operation. The third model tests the technical efficiency of the key revenues and expenditures. The results in the static models estimate the average technical efficiency of municipal libraries in the interval (0.691–0.759) for the input-oriented models, and in the interval (1.413–2.005) for the output-oriented models. In the dynamic models, the majority of municipal libraries in 2015 showed lower technical efficiency and productivity in comparison with the year of 2011. The factors influencing the level of efficiency and its course include the inputs and the outputs, and their combinations within individual models.     

The effects of handling outliers on the performance of bankruptcy prediction models

Ratio type financial indicators are the most popular explanatory variables in bankruptcy prediction models. These measures often exhibit heavily skewed distribution because of the presence of outliers. In the absence of clear definition of outliers, ad hoc approaches can be found in the literature for identifying and handling extreme values. However, it is not clear how these different approaches can affect the predictive power of models. There seems to be consensus in the literature on the necessity of handling outliers, at the same time, it is not clear how to define extreme values to be handled in order to maximize the predictive power of models. There are two possible ways to reduce the bias originating from outliers: omission and winsorization. Since the first approach has been examined previously in the literature, we turn our attention to the latter. We applied the most popular classification methodologies in this field: discriminant analysis, logistic regression, decision trees (CHAID and CART) and neural networks (multilayer perceptron). We assessed the predictive power of models in the framework of tenfold stratified crossvalidation and area under the ROC curve. We analyzed the effect of winsorization at 1, 3 and 5% and at 2 and 3 standard deviations, furthermore we discretized the range of each variable by the CHAID method and used the ordinal measures so obtained instead of the original financial ratios. We found that this latter data preprocessing approach is the most effective in the case of our dataset. In order to check the robustness of our results, we carried out the same empirical research on the publicly available Polish bankruptcy dataset from the UCI Machine Learning Repository. We obtained very similar results on both datasets, which indicates that the CHAID-based categorization of financial ratios is an effective way of handling outliers with respect to the predictive performance of bankruptcy prediction models.     

Q‐FACTORS IN EMPIRICAL ASSET PRICING: A REVIEW AND SYNTHESIS

The latest development in the asset pricing literature is the emergence of empirical asset pricing models comprising q‐factors (profitability and investment factors) in conjunction with other factors. However, as in the case of the older empirical models, there is scepticism regarding the application of these newer factor models consisting of q‐factors because of the debate surrounding the explanatory power of these empirically inspired asset pricing models. This review attempts to synthesize studies pertaining to the four alternative explanations of the asset pricing models comprising the q‐factors (profitability and investment) – the data snooping hypothesis, the risk‐based explanation, the irrational investor behaviour explanation and the interpretation that suggest that the combination of the risk‐free asset and the factors comprising the model span the mean‐variance efficient tangency portfolio that prices the universe of assets.     

Crowdsourcing-based semantic relation recognition for natural language questions over RDF data

ABSTRACT

Natural language query systems over RDF data need to rely on the semantic relations in query. First, we propose the new crowdsourcing model that used to produce semantic relations dataset. The model not only inherits completeness of the iterative model and accuracy of the parallel model, but also saves human resources. Second, we mine the rules of semantic relation recognition from the correlations between dependency structures and semantic relations. Third, we propose an algorithm of semantic relation recognition for natural language query over RDF data, and experiments demonstrate that it can recognize more semantic relations than existing methods.     

A systematic mapping study on enterprise architecture mining

Enterprise architecture allows companies to deal with digital transformation through the proactive presentation and alignment of business and IT in a holistic manner. One important challenge is EA modelling since it is time-consuming (thus expensive), error-prone, and biased owing to experts’ subjective opinions. This challenge might consequently be addressed through automatic EA modelling. We conducted a systematic mapping study to classify and evaluate the research concerning EA mining proposals developed specifically for TOGAF and Archimate. After analysing results, we concluded that the research field is not sufficiently mature and further research on EA mining is necessary.     

BENCHMARKING LARGE ACCOUNTING FRAMEWORKS: A GENERALIZED MULTIVARIATE MODEL

We present a multivariate benchmarking model for achieving consistency between large quarterly and annual accounting frameworks. The method is based on a quadratic optimization problem, for which many efficient numeric solvers exist. The method combines several features, such as linear constraints, ratio constraints, weights, and inequalities, in one model. Therefore, a wide range of modelling possibilities is supported. This method is especially interesting for national statistical offices, to simplify their processes to achieve consistency between publications.