Evaluation of multiple-imputation procedures for three-mode component models

Three-mode analysis is a generalization of principal component analysis to three-mode data. While two-mode data consist of cases that are measured on several variables, three-mode data consist of cases that are measured on several variables at several occasions. As any other statistical technique, the results of three-mode analysis may be influenced by missing data. Three-mode software packages generally use the expectation–maximization (EM) algorithm for dealing with missing data. However, there are situations in which the EM algorithm is expected to break down. Alternatively, multiple imputation may be used for dealing with missing data. In this study we investigated the influence of eight different multiple-imputation methods on the results of three-mode analysis, more specifically, a Tucker2 analysis, and compared the results with those of the EM algorithm. Results of the simulations show that multilevel imputation with the mode with the most levels nested within cases and the mode with the least levels represented as variables gives the best results for a Tucker2 analysis. Thus, this may be a good alternative for the EM algorithm in handling missing data in a Tucker2 analysis.     

Dynamic collaboration between small- and medium-sized enterprises from highly dissimilar markets

Emerging markets have created significant opportunities for European small- and medium-sized enterprises (SMEs). Highly dissimilar market contexts compared to traditional markets and missing resources present substantial barriers to successfully serve those markets. Collaborative partnerships with local SMEs can help to overcome the barriers. Knowledge on how SMEs can develop lasting collaborative relationships in highly dissimilar contexts is scarce. This research investigates which factors influence the stability of more informal collaborative partnerships of Central European SMEs with SMEs located in China. A qualitative analysis of four cases confirms the importance of complementarity of partner resources, working well together based on mutual trust and commitment, shared sensemaking, and the balanced consideration of partner interests. Mutual learning and adaptation in iterative steps to enhance shared sensemaking and seeing differences in interpretation and behavior arising from contextual dissimilarity as opportunities instead of threats turn out to be SME-specific factors essential for building stable informal collaborative relations.     

纳米技术与隐私:有关全景敞视监狱外的持续监视

我们认为,隐形标签、传感器和射频识别芯片等形式的纳米技术将在两个方面引发不同于过去几十年中传统意义上的隐私问题。一是它们将不再仅仅围绕监视集中和权力集中的理念,如全景敞视监狱的隐喻含义,而是要关注分散层面上的持续观察;二是对隐私的关注可能不仅仅涉及对信息流动的制约,同时也涉及到相关材料和纳米制品,如芯片及标签的设计。我们首先建构了一个框架用以概述当前有关隐私问题的辩论情况,进而提出我们的基本观点。     

The DYNAMO-HIA Model: An Efficient Implementation of a Risk Factor/Chronic Disease Markov Model for Use in Health Impact Assessment (HIA)

In Health Impact Assessment (HIA), or priority-setting for health policy, effects of risk factors (exposures) on health need to be modeled, such as with a Markov model, in which exposure influences mortality and disease incidence rates. Because many risk factors are related to a variety of chronic diseases, these Markov models potentially contain a large number of states (risk factor and disease combinations), providing a challenge both technically (keeping down execution time and memory use) and practically (estimating the model parameters and retaining transparency). To meet this challenge, we propose an approach that combines micro-simulation of the exposure information with macro-simulation of the diseases and survival. This approach allows users to simulate exposure in detail while avoiding the need for large simulated populations because of the relative rareness of chronic disease events. Further efficiency is gained by splitting the disease state space into smaller spaces, each of which contains a cluster of diseases that is independent of the other clusters. The challenge of feasible input data requirements is met by including parameter calculation routines, which use marginal population data to estimate the transitions between states. As an illustration, we present the recently developed model DYNAMO-HIA (DYNAMIC MODEL for Health Impact Assessment) that implements this approach.     

SORTING AND THE OUTPUT LOSS DUE TO SEARCH FRICTIONS

We analyze a general search model with on‐the‐job search (OJS) and sorting of heterogeneous workers into heterogeneous jobs. For given values of nonmarket time, the relative efficiency of OJS, and the amount of search frictions, we derive a simple relationship between the unemployment rate, mismatch, and wage dispersion. We estimate the latter two from standard micro data. Our methodology accounts for measurement error, which is crucial to distinguish true from spurious mismatch and wage dispersion. We find that without frictions, output would be about 9.5% higher if firms can commit to pay wages as a function of match quality and 15.5% higher if they cannot. Noncommitment leads to a business‐stealing externality which causes a 5.5% drop in output.     

The Size and Power of Bootstrap and Bartlett-Corrected Tests of Hypotheses on the Cointegrating Vectors

In this paper we compare Bartlett-corrected, bootstrap, and fast double bootstrap tests on maximum likelihood estimates of cointegration parameters. The key result is that both the bootstrap and the Bartlett-corrected tests must be based on the unrestricted estimates of the cointegrating vectors: procedures based on the restricted estimates have almost no power. The small sample size bias of the asymptotic test appears so severe as to advise strongly against its use with the sample sizes commonly available; the fast double bootstrap test minimizes size bias, while the Bartlett-corrected test is somehow more powerful.     

The consensus value: a new solution concept for cooperative games

To generalize the standard solution for 2-person TU games into n-person cases, this paper introduces a recursive two-sided negotiation process to establish cooperation between all players. This leads to a new solution concept for cooperative games: the consensus value. An explicit comparison with the Shapley value is provided, also at the axiomatic level. Moreover, a class of possible generalizations of the consensus value is introduced and axiomatized with the Shapley value at one end and the equal surplus solution at the other. Finally, we discuss a non-cooperative mechanism which implements the consensus value.