Modified maximum spacings method for generalized extreme value distribution and applications in real data analysis

This paper analyzes weekly closing price data of the S&P 500 stock index and electrical insulation element lifetimes data based on generalized extreme value distribution. A new estimation method, modified maximum spacings (MSP) method, is proposed and obtained by using interior penalty function algorithm. The standard error of the proposed method is calculated through Bootstrap method. The asymptotic properties of the modified MSP estimators are discussed. Some simulations are performed, which show that the proposed method is not only available for the whole shape parameter space, but is also of high efficiency. The benchmark risk index, value at risk (VaR), is evaluated according to the proposed method, and the confidence interval of VaR is also calculated through Bootstrap method. Finally, the results are compared with those derived by empirical calculation and some existing methods.     

Asymptotic distributions in random graphs with applications to social networks

Various kinds of subgraph counts have been proposed as important statistics in the social sciences: for instance, in connection with studies of the structural properties of social networks. Since the empirical structure in question often involves an element of randomness, subgraph counts are random variables and, consequently, we need to describe their probabilistic properties. In this paper we give a survey of results dealing with the asymptotic distributions of general subgraph counts for a number of standard graph distributions. Although we do not include proofs for all the results, we illustrate the methodology used through studies of asymptotic behaviour for certain subgraph counts.     

How to reduce your inventory: a real world case study

This case study describes the use of a performance analysis system at the Safety Products Division of Mine Safety Appliances Company, which contributed to the reduction of excess inventories by more than $8,000,000 during the first two years of implementation.     

Efficient big data model selection with applications to fraud detection

As the volume and complexity of data continues to grow, more attention is being focused on solving so-called big data problems. One field where this focus is pertinent is credit card fraud detection. Model selection approaches can identify key predictors for preventing fraud. Stagewise Selection is a classic model selection technique that has experienced a revitalized interest due to its computational simplicity and flexibility. Over a sequence of simple learning steps, stagewise techniques build a sequence of candidate models that is less greedy than the stepwise approach.This paper introduces a new stochastic stagewise technique that integrates a sub-sampling approach into the stagewise framework, yielding a simple tool for model selection when working with big data. Simulation studies demonstrate the proposed technique offers a reasonable trade off between computational cost and predictive performance. We apply the proposed approach to synthetic credit card fraud data to demonstrate the technique’s application.     

Two applications of the Points-of-View model to subject variations in sorting data

In many of the social sciences it is useful to explore the “working models” or mental schemata that people use to organise items from some cognitive or perceptual domain. With an increasing number of items, versions of the Method of Sorting become important techniques for collecting data about inter-item similarities. Because people do not necessarily all bring the same mental model to the items, there is also the prospect that sorting data can identify a range within the population of interest, or even distinct subgroups. Anthropology provides one tool for this purpose in the form of Cultural Consensus Analysis (CCA). CCA itself proves to be a special case of the “Points of View” approach. Here factor analysis is applied to the subjects’ method-of-sorting responses, obtaining idealized or prototypal modes of organising the items—the “viewpoints”. These idealised modes account for each subject’s data by combining them in proportions given by the subject’s factor loadings. The separate organisation represented by each viewpoint can be made explicit with clustering or multidimensional scaling. The technique is illustrated with job-sorting data from occupational research, and social-network data from primate behaviour.     

Applying real options theory to HRM: an empirical study of IT software firms in India

Environmental uncertainties can impact the market value of a firm's human assets both positively and negatively, and make return on human assets uncertain over time. However, the strategic human resource management (SHRM) literature has so far focused almost exclusively only on the upside value of human assets of a firm. Real options theory can provide the process heuristics as well as the economic logic for guiding investments in human assets to create sustainable market value for firms operating in uncertain environments. In spite of the growth in popularity of the real options approach, no meaningful progress, however, has been made towards application of this approach to HRM. This study, using data from 108 IT software development firms in India, seeks to address this gap and make three important contributions to the SHRM literature: (1) operationalise the concept of HR options by identifying the HR practices that possess option value; (2) investigate how use of HR options affects firm-level performance; and (3) develop and test a causal model that links the various types of HR options that firms use to exploit uncertainties faced by them with the firm-level operational and financial outcomes. The results support the central hypothesis of the article that use of HR options by firms operating in uncertain environments would have positive impact on their operational and financial performance. Significant differences were observed in the nature of linkages between different types of HR options used to address different types of uncertainties, and the operational and financial performance of the firm.     

FRED-SD: A real-time database for state-level data with forecasting applications

We construct a real-time dataset (FRED-SD) with vintage data for the U.S. states that can be used to forecast both state-level and national-level variables. Our dataset includes approximately 28 variables per state, including labor-market, production, and housing variables. We conduct two sets of real-time forecasting exercises. The first forecasts state-level labor-market variables using five different models and different levels of industrially disaggregated data. The second forecasts a national-level variable exploiting the cross-section of state data. The state-forecasting experiments suggest that large models with industrially disaggregated data tend to have higher predictive ability for industrially diversified states. For national-level data, we find that forecasting and aggregating state-level data can outperform a random walk but not an autoregression. We compare these real-time data experiments with forecasting experiments using final-vintage data and find very different results. Because these final-vintage results are obtained with revised data that would not have been available at the time the forecasts would have been made, we conclude that the use of real-time data is essential for drawing proper conclusions about state-level forecasting models.     

Correction to: Determinants of agricultural entrepreneurship: a GEM data based study

International Entrepreneurship and Management Journal - The original article has been corrected. The spelling of author name Amit Kumar Dviwedi was incorrect and has now been corrected to Amit...     

Measuring and forecasting retail trade in real time using card transactional data

We build big data retail trade indicators for Spain using high-dimensional card transaction data from one of the country’s biggest banks. The resulting indicators replicate the dynamics of the Spanish retail trade indices (RTI), regional RTIs (Spain’s autonomous regions), and RTI by retailer type (distribution classes) released by the Spanish National Statistics Institute. The new indicators not only have a higher frequency (daily data) and higher geographical and sectorial breakdown but are also shown to improve nowcasting and forecasting power for the official RTI, making them key variables to monitor consumption.     

把绚烂的品牌之梦变成现实

[主持人语] 　　关于品牌和品牌战略,20多年来,它对中国的企业来说,已经是耳熟能详的词语了.世界经济的发展清楚地告诉我们,品牌不仅维系着一个企业的命运,也维系着一个民族、一个国家的兴衰.丰田、松下、西门子、诺基亚、波音等,一批耀眼夺目的世界强势品牌,不仅让它们的企业傲视群雄,更令它们的国家为之自豪.……     

Registration of longitudinal data by means of musical notation: some applications

Graphic representation of complicated courses is often necessary to detect patterns that may be worth analysing. Examples are given to show how musical notation or modifications of musical notation may be used to register courses (or cross-sectional data) with more variables than usual. One can register courses with known duration of components (and then also simultaneities); the time scale may be defined according to data. One can also register sequences without known duration of components. Finally the method can be modified so as to suit cross-sectional data. The method can be used to register a single case but also a group of cases that are thus rendered comparable. It is a method of registration, not of analysis but one that may help prepare a refined analysis.     

More powerful panel data unit root tests with an application to mean reversion in real exchange rates

Unit root tests, seeking mean or trend reversion, are frequently applied to panel data. We show that more powerful variants of commonly applied tests are readily available. Moreover, power gains persist when the modifications are applied to bootstrap procedures that may be employed when cross‐correlation of a rather general sort among individual panel members is suspected. Copyright © 2004 John Wiley & Sons, Ltd.     

A study on X party material flow: the theory and applications

A thorough study of X party material flow (XPMF), its theory and its applications is conducted in this research. The X material flow concept is an extension of the material flow (MF) theory. To further elucidate that XPMF is one type of MF service model with PMF (party, material, flow) fractal structure and the characteristics of XPMF, we develop the three-pyramid synergetic operational model of XPMF. Through the analysis of several cases, we believe that OIR (Objective relational grade, Information sharing grade, and Resource complementary grade) is the set of order parameters that control and determine the formation of XPMF new structure and its degree of being ordered. Therefore it reveals the mechanism of XPMF formation and evolution. We also provide the principles and the methods for XPMF control.     

A score test for non‐nested hypotheses with applications to discrete data models

In this paper it is shown that a convenient score test against non‐nested alternatives can be constructed from the linear combination of the likelihood functions of the competing models. This is essentially a test for the correct specification of the conditional distribution of the variable of interest. Given its characteristics, the proposed test is particularly attractive to check the distributional assumptions in models for discrete data. The usefulness of the test is illustrated with an application to models for recreational boating trips. Copyright © 2001 John Wiley & Sons, Ltd.     

Decomposing real and deflator-based government growth: an application to the American states

In recent years the question of how the size of government should be measured has been an unresolved issue in the research program on government growth. Employing the simple ratio of government spending to total economic output in their measures, most scholars have failed to recognize the different inflation rates which characterize the public and private sectors, as well as the fact that observed government growth may be attributable both to the effects of differential deflators and to increases in the scope of government activity. In this paper I present a simple geometric formulation for decomposing government growth into real growth and deflator-based components. Although the utility of this method is illustrated using data from two American states (New York and Florida), the technique has broad applicability for decomposing government growth into real and deflator components for a wide range of political systems for which data are available.     

Income: the real and the incomparable

The main propositions of this paper are: inter-temporal and interspatial comparisons lose meaning when the arrays of goods and services being priced are too dissimilar; the composition and pricing of GDP are a function of income distribution; income tends to circulate among persons of broadly similar wealth and culture; a significant part of what is recorded as final household expenditure is intermediate in nature; public expenditure is intrinsically intermediate, and poses a variety of conceptual difficulties. Growth is associated with changes which undermine the validity of its measurement.     

On corporate structure,strategy, and performance: a study with directed acyclic graphs and PC algorithm

This paper reconsiders empirical evidence on relationships among variables related to corporate strategy, structure, and performance. Causal relationships among variables are modeled as directed acyclic graphs using PC‐algorithm. Return on Assets appears to be determined by Advertising Intensity, Unrelated Diversification, R&D Intensity, and Organizational Ownership Hierarchy. Debt Structure and Investor Characteristics do not cause (either directly or indirectly) return on assets. These latter two variables appear to be effects of return on assets, not causes. Results offer mixed support of the theory that structure causes strategy, which in turn causes performance. Copyright © 2009 John Wiley & Sons, Ltd.