Residential investment and recession predictability

We assess the importance of residential investment for the prediction of economic recessions for an unbalanced panel of 12 OECD countries over the period 1960Q1–2014Q4. Our approach is to estimate various probit models with different leading indicators and evaluate their relative prediction accuracies using the area under the receiver operating characteristic curve as our forecasting performance metric. We document that residential investment contains information that is useful for predicting recessions both in-sample and out-of-sample. This result is robust to adding typical leading indicators, such as the term spread, stock prices, consumer confidence surveys and oil prices. It is shown that residential investment is particularly useful for the prediction of recessions for countries with high home-ownership rates. Finally, in a separate exercise for the US, we show that the predictive ability of residential investment is — in a broad sense — robust to employing real-time data.     

High-involvement information sharing practices: an international perspective

The type of information shared within organizations is a key aspect of strategic human resources management. In this exploratory study, we expand our understanding of high-performance work systems (HPWSs) by examining the information sharing practices of firms operating in different countries to assess the extent that high-involvement-oriented information sharing in organizations is influenced by home-country economic structures and national cultural factors. We assess firm-level data from a 12-country sample to evaluate the use of formal information sharing practices for non-managerial employees. The results of our analysis support the contention that information sharing practices varies by country, and that cultural similarities based on geographical region exist within the three information content areas of business strategy, financial performance, and organization of work. Specifically, we found significant variance in information sharing practices of firms based in different countries but homogeneity of practices among geographical neighbors. There is a modest positive relationship between sharing business strategy information and perceived firm performance that is significantly stronger in North America compared to other regions. These results have important implications for the ongoing theoretical development of mechanisms underlying the use of HPWS practices in an international context.     

客户关系管理中的混合数据聚类应用研究

聚类分析是客户关系管理（CRM）中非常重要的工具之一．提出一种新的算法，k-centers，通过重新定义相似度衡量和聚类中心更新方法，能够有效处理混合类型的CRM数据。以解决经典算法比如k-means等只适用于数值类型数据的不足，实验分析说明k-tenters算法能够准确把握不同类型客户的特征，为个性化的市场营销提供有力支持。     

Bridging macroeconomic data between statistical classifications: the count-seed RAS approach

ABSTRACT

In applications, it is often necessary to link heavily aggregated macroeconomic datasets adhering to different statistical classifications. We propose a simple data reclassification procedure for those cases in which a bridge matrix grounded in microdata is not available. The essential requirement of our approach, which we refer to as count-seed RAS, is that there exists a time period or a geographical entity similar to the one of interest for which the relevant economic variable is observed according to both classifications. From this information, a bridge matrix is constructed using bi-proportional methods to rescale a seed matrix based on a qualitative correspondence table from official sources. We test the procedure in two case studies and by Monte Carlo methods. We find that, in terms of reclassification accuracy, it performs noticeably better than other expeditious methods. The analytical framework underlying our approach may prove a useful way of conceptualizing data reclassification problems.     

Forecast combinations under structural break uncertainty

This paper proposes two new weighting schemes that average forecasts based on different estimation windows in order to account for possible structural change. The first scheme weights the forecasts according to the values of reversed ordered CUSUM (ROC) test statistics, while the second weighting method simply assigns heavier weights to forecasts that use more recent information. Simulation results show that, when structural breaks are present, forecasts based on the first weighting scheme outperform those based on a procedure that simply uses ROC tests to choose and forecast from a single post-break estimation window. Combination forecasts based on our second weighting scheme outperform equally weighted combination forecasts. An empirical application based on a NAIRU Phillips curve model for the G7 countries illustrates these findings, and also shows that combination forecasts can outperform the random walk forecasting model.     

Research efficiency analysis of China's university faculty members: A modified meta-frontier DEA approach

Traditionally, data envelopment analysis (DEA) requires all decision-making units (DMUs) to have similar characteristics and experiences within the same external conditions. In many cases, this assumption fails to hold, and thus, difficulties will be encountered to some extent when measuring efficiency with a standard DEA model. Ideally, the performance of DMUs with different characteristics could be examined using the DEA meta-frontier framework. However, some of these DMUs are mixed-type DMUs that may affiliate with more than one group. Furthermore, the total number of observations of these mixed-type DMUs is limited. This is one of the common problems when studies focus on faculty research performance in higher education institutions. In general, a faculty member is affiliated with a certain department, and if the departmental assessment policy is not suitable for faculty members who are involved in interdisciplinary research, their performance could be underestimated. Therefore, the proposed model is an extension of the DEA meta-frontier framework that can assess the performance of mixed-type DMUs by constructing the reference set without the same type of DMUs. In this paper, the scientific research efficiency of faculty members at the Inner Mongolia University is used as an example to provide a better understanding of the proposed model. The proposed model is intended to provide a fair and balanced performance assessment method that reflects actual performance, especially for mixed-type DMUs.     

The measurement of human capital: a multivariate macro-approach

We examine the human capital status of 123 countries by employing factor analysis on various national human capital indicators for the period 2000–2008 to construct two new measures. The first measure is based on advanced human capital, while the second is based on basic human capital. Our measures differ substantially from indicators used in previous studies on human capital and also lead to different rankings of countries. As rankings are not that informative without further information, we analyzed the distance between each country and the sample mean. Differences between countries are much more pronounced for our measure on advanced human capital than for our measure on basic human capital. Using cluster analysis, we classified the countries in four homogenous groups.     

Bayes estimates of muIti-criteria decision alternatives using Monte Carlo integration

A Bayesian procedure is proposed for the estimation of the weights of the alternatives in a multi-criteria decision model with data that stem from pair-wise comparison of alternatives. The prior information restricts the weights to the unit simplex. The posterior results are computed by Monte Carlo integration procedures based on importance sampling. The Bayesian procedure is applied to a case study concerning the choice of a professor of Operations Research (OR). Results are: (1) according to the Bayesian procedure a different candidate would be chosen as professor of OR than according to the maximum likelihood procedure; (2) given the prior and data information, there exists a substantial probability of taking the wrong decision; (3) there exists a ranking of the candidates with a posterior probability greater than one half.     

Community structure in the World Trade Network based on communicability distances

In this paper, we investigate the mesoscale structure of the World Trade Network. In this framework, a specific role is assumed by short- and long-range interactions, and hence by any suitably defined network-based distance between countries. Therefore, we identify clusters through a new procedure that exploits Estrada communicability distance and the vibrational communicability distance, which turn out to be particularly suitable for catching the inner structure of the economic network. The proposed methodology aims at finding the distance threshold that maximizes a specific quality function defined for general metric spaces. Main advantages regard the computational efficiency of the procedure as well as the possibility to inspect intercluster and intracluster properties of the resulting communities. The numerical analysis highlights peculiar relationships between countries and provides a rich set of information that can hardly be achieved within alternative clustering approaches.

     

The Information Content of Productivity Measures: an International Comparison*

This study examines whether, and to what extent, productivity information is used by the market and whether it is useful as a predictive measure. To see if nationality has any bearing on these issues, we examine the information content of productivity measures in Japan, Korea and the United States. While correlations between security returns, productivity changes and unexpected earnings are not uniform across our sample countries, our findings suggest that productivity, as a performance metric, merits greater attention by financial managers and security analysts. The substitution of productivity in place of short-run earnings could very well be the key to enhanced performance in today's environment of global competition.     

A computational intelligent approach to multi-factor analysis of violent crime information system

Various scientific studies have explored the causes of violent behaviour from different perspectives, with psychological tests, in particular, applied to the analysis of crime factors. The relationship between bi-factors has also been extensively studied including the link between age and crime. In reality, many factors interact to contribute to criminal behaviour and as such there is a need to have a greater level of insight into its complex nature. In this article we analyse violent crime information systems containing data on psychological, environmental and genetic factors. Our approach combines elements of rough set theory with fuzzy logic and particle swarm optimisation to yield an algorithm and methodology that can effectively extract multi-knowledge from information systems. The experimental results show that our approach outperforms alternative genetic algorithm and dynamic reduct-based techniques for reduct identification and has the added advantage of identifying multiple reducts and hence multi-knowledge (rules). Identified rules are consistent with classical statistical analysis of violent crime data and also reveal new insights into the interaction between several factors. As such, the results are helpful in improving our understanding of the factors contributing to violent crime and in highlighting the existence of hidden and intangible relationships between crime factors.     

Relative pricing of binary options in live soccer betting markets

Live soccer betting markets differ from other binary options markets in that all fundamental information is observable, the options mature in less than two hours and the markets are highly liquid. This study presents a new method for the identification of hidden information in market prices. The method is based on two independent Poisson distributions and on a numerical algorithm for the aggregation of all market price information into one rational number. The method is applied to an empirical dataset of real time market prices in 29,413 soccer games. The results indicate that the method selects the most profitable markets and allows for a significant improvement in average investment returns.     

A versatile and robust metric entropy test of time-reversibility,and other hypotheses

We examine the performance of a metric entropy statistic as a robust test for time-reversibility (TR), symmetry, and serial dependence. It also serves as a measure of goodness-of-fit. The statistic provides a consistent and unified basis in model search, and is a powerful diagnostic measure with surprising ability to pinpoint areas of model failure. We provide empirical evidence comparing the performance of the proposed procedure with some of the modern competitors in nonlinear time-series analysis, such as robust implementations of the BDS and characteristic function-based tests of TR, along with correlation-based competitors such as the Ljung–Box Q-statistic. Unlike our procedure, each of its competitors is motivated for a different, specific, context and hypothesis. Our evidence is based on Monte Carlo simulations along with an application to several stock indices for the US equity market.     

基于SSE指令集的运动目标模板匹配算法设计

本文针对序列图像中运动目标识别的广泛需求,分析了模板匹配算法的实现过程及特点,研究、设计了一种基于SSE指令集的运动目标模板匹配算法。该算法利用SSE4指令集实现多位数据并行计算,显著提高了模板匹配算法的处理速度。实验结果表明,该算法对于序列图像中的运动目标识别具有速度快、识别精度高的特点,具有一定的应用价值。     

基于改进的SVM的交通信息融合方法研究

在对交通数据特性分析的基础上,提出基于改进的SVM多源交通信息融合算法。首先,在分析了传统SVM不足的基础上,提出了决策树-支持向量机算法(Decision TreeMethod-SupportVector Machines,DTM-SVM)。其次,对DTM-SVM多源信息融合算法的模型及其流程进行了研究,建立了DTM-SVM的分层结构模型。最后,经过实际数据测试,证明了该方法的高效性和实用性。     

Tracking world trade and GDP in real time

This paper proposes a simple procedure for obtaining monthly assessments of short-run perspectives for quarterly world GDP and trade. It combines high-frequency information from emerging and advanced countries so as to explain quarterly national accounts variables through bridge models. The union of all bridge equations leads to our world bridge model (WBM). The WBM approach of this paper is new for two reasons: its equations combine traditional short-run bridging with theoretical level-relationships, and it is the first time that forecasts of world GDP and trade have been computed for both advanced and emerging countries on the basis of a real-time database of approximately 7000 time series. Although the performances of the equations that are searched automatically should be taken as a lower bound, our results show that the forecasting ability of the WBM is superior to the benchmark. Finally, our results confirm that the use of revised data leads to models’ forecasting performances being overstated significantly.     

The demand for housing in developing countries: The case of Korea

This paper presents the results of an analysis of urban housing demand for Korea taking into account the most recent findings of housing demand analysis concerning specification and aggregation biases. In order to obtain correctly specified demand functions, a procedure based on a model of the housing market originally proposed by Muth is used. Drawing on the detailed land information available in Korea, this procedure permits the calculation of an individual price per unit of housing services for each household. The results show conclusively that both the income and price elasticity of the demand for housing services in Korea are comparable to those found in the United States: the income elasticity is smaller than one and the price elasticity is negative and smaller than one in absolute value. Given the number of countries found within the per capita income range between Korea ($700) and the United States ($7800), the finding that these two countries have comparable demand elasticities is of major significance: in the absence of good national estimates, the order of magnitudes found here would be used for other country analyses.     

农业全产业链发展的问题和建议

农业自古以来都是民生之本,是我国的第一产业,也是我国国民经济发展的基础。文章通过现有的理论研究,首先分析国外部分发达国家的农业发展模式,再结合我国的传统农业产业链存在的诸多问题,如层级过多、信息不对称、风险规避能力弱、缺乏消费者视角等,借鉴发达国家的经验,以大数据和信息资源的整合为侧重点,提出构建农业全产业链平台的方案。通过整合上下游企业的多方资源,改善和优化产业链,为现代农业的发展提供一些参考。     

司法会计鉴定标准分歧的解决——基于优势信息主体决策规则

司法会计鉴定业务同时涉及会计标准和法律标准的运用问题。在两个标准有分歧的情况下,对同一事物性质的鉴定只能依据一种标准。会计人员与司法人员在对涉案会计事项鉴定中,不同的标准会交替成为相对的优势信息主体:在会计事项鉴定中,会计人员是当然的优势信息主体,必然遵循会计标准;在会计事项鉴定后的法律适用及其责任判定的司法活动中,司法人员是当然的优势信息主体,必然遵循法律标准进行判定。因此,基于优势信息主体决策规则,这两个标准的分歧可以达到统一。     

基于树算法的TSP问题的一个界

对于满足三角不等式的TSP问题,已经有了多种算法,对于我们已经知道的树算法而言,一般文献上都已经证明为一个3/2算法,但本文通过分析和证明,得出了该算法的一个更小界:3/2-3/2n。