Labor market analysis through transformations and robust multivariate models

The work presents a robust approach to labor share analysis. The estimate of labor share presents various complexities related to the nature of the data sets to be analyzed. Typically, labor share is evaluated by using discriminant analysis and linear or generalized linear models, that do not take into account the presence of possible outliers. Moreover, the variables to be considered are often characterized by a high dimensional structure. The proposed approach has the objective of improving the estimation of the model using robust multivariate regression techniques and data transformation.     

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。     

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.     

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.     

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.     

Dispatching and routing of emergency vehicles in disaster mitigation using data fusion

The aim of this paper is to develop a robust methodology for the dispatching and routing of emergency vehicles (EVs) in a post-disaster environment with the support of data fusion. In this work, we consider an earthquake scenario with a large number of casualties needing medical attention. Given an influx of information (regarding casualties, road, traffic conditions, etc.), data are fused to provide estimates of the entities under consideration. We use this information to dispatch and route EVs to casualty pickup locations, followed by delivery to appropriate hospitals. Key factors here include patient priorities, clustering criteria, and distance. Similarly, factors affecting the dispatching of EVs from patient locations to hospitals include waiting time at hospital emergency rooms, hospital capacity, and distance. Routes must be generated for EVs by accounting for real-world road networks, existing road damage, congestion, and related issues. We develop a dispatching and routing simulation model, and utilize a case study to evaluate the performance of our proposed methodology.     

遗传算法及其在客户关系管理中的应用研究

遗传算法是一种宏观意义下的仿生优化算法，它模拟生物在自然环境中的遗传和进化过程而形成的一种自适应全局优化概率搜索算法。这种算法可以用于对海量的客户信息进行搜索，提取对决策有用的信息，让企业在激烈的市场竞争中处于有利地位，最终达到最高利润。本文还通过一个简单的实例研究将该算法应用在客户关系管理中。     

A new method for measuring congestion in data envelopment analysis

In data envelopment analysis (DEA), there are two principal methods for identifying and measuring congestion: Those of Färe et al. [Färe R, Grosskopf S. When can slacks be used to identify congestion. An answer to W. W. Cooper, L. Seiford, J. Zhu. Socio-Economic Planning Sciences 2001;35:1–10] and Cooper et al. [Cooper WW, Deng H, Huang ZM, Li SX. A one-model approach to congestion in data envelopment analysis. Socio-Economic Planning Sciences 2002;36:231–8]. In the present paper, we focus on the latter work in proposing a new method that requires considerably less computation. Then, by proving a selected theorem, we show that our proposed methodology is indeed equivalent to that of Cooper et al.     

流动性约束下我国居民消费行为的二元结构与地区差异

本文构建了状态空间模型和跨省面板数据模型,就不确定性和流动性约束等对我国居民消费行为影响进行比较分析,结果表明:1978年以来,收入的不确定性对城镇居民消费行为的负面影响要大于农村居民;城镇居民面临的流动性约束相对小于农村居民;城镇居民面对利率的替代效应要大于收入效应,而农村居民与之相反。1995年以来,收入和支出的不确定性对我国居民整体消费行为的影响并不显著,但与东中部地区居民消费变动正相关;我国居民消费面临的流动约束整体来看并不严重,但中部地区居民相对而言面临更强的流动性约束。