Is the German system of corporate governance converging towards the Anglo-American model?

This paper analyses whether the German corporate governance is converging towards Anglo-American practices. We summarise the extant empirical evidence on the various governance mechanisms that economic theory suggests ensure efficiency and describe recent legal developments. We find no clear signs of convergence in form, i.e. the main distinctive features of the German system have remained largely unaltered. However, changes occurred over the last decade (specially in the legal framework) suggest a certain convergence in function, i.e. some governance mechanisms have effectively incorporated aims and/or goals generally associated with the Anglo-American model.

Complete ranking procedures with appropriate loss functions

This paper treats the problem of comparing different evaluations of procedures which rank the variances of k normal populations. Procedures are evaluated on the basis of appropriate loss functions for a particular goal. The goal considered involves ranking the variances of k independent normal populations when the corresponding population means are unknown. The variances are ranked by selecting samples of size n from each population and using the sample variances to obtain the ranking. Our results extend those of various authors who looked at the narrower problem of evaluating the standard proceduv 3 associated with selecting the smallest of the population variances (see e.g.,P. Somerville (1975)).

Different loss functions (both parametric and non-parametric) appropriate to the particular goal under consideration are proposed. Procedures are evaluated by the performance of their risk over a particular preference zone. The sample size n, the least favorable parametric configuration, and the maximum value of the risk are three quantities studied for each procedure. When k is small these quantities, calculated by numerical simulation, show which loss functions respond better and which respond worse to increases in sample size. Loss functions are compared with one another according to the extent of this response. Theoretical results are given for the case of asymptotically large k. It is shown that for certain cases the error incurred by using these asymptotic results is small when k is only moderately large.

This work is an outgrowth of and extends that of J. Reeves and M.J. Sobel (1987) by comparing procedures on the basis of the sample size (perpopulation) required to obtain various bounds on the associated risk functions. New methodologies are developed to evaluate complete ranking procedures in different settings.     

A note on the rubustness of the lilliefors test for univariate normality with respect to equicorrelated data

The Lilliefors test, which was developed by Lilliefors (1967), is a well-known test for univariate normality when population parameters are unknown. The main assumption for implementing the test is the independent-data assumption. This paper demonstrates the robustness of the Lilliefors test against equicorrelated observations. More specifically, we show that the null distribution of the Lilliefors test statistic is invariant under the alternate assumption that the observations are equicorrelated.     

A New Approach to the Estimation of Inter-Variable Correlation

The use of different measures of similarity between observed vectors for the purposes of classifying or clustering them has been expanding dramatically in recent years. One result of this expansion has been the use of many new similarity measures, designed for the purpose of satisfying various criteria. A noteworthy application involves estimating the relationships between genes using microarray experimental data. We consider the class of ‘correlation-type’ similarity measures. The use of these new measures of similarity suggest that the whole problem needs to be formulated in statistical terms to clarify their relative benefits. Pursuant to this need, we define, for each given observed vector, a baseline representing the ‘true’ value common to each of the component observations. These ‘true’ values are taken to be parameters. We define the ‘true correlation’ between each two observed vectors as the average (over the distribution of the observations for given baseline parameters) of Pearson's correlation with sample means replaced by the corresponding baseline parameters. Estimators of this true correlation are assessed using their mean squared error (MSE). Proper Bayes estimators of this true correlation, being based on the predictive posterior distribution of the data, are both difficult to calculate/analyze and highly non robust. By constrast, empirical Bayes estimators are: (i) close to their Bayesian counterparts; (ii) easy to analyze; and (iii) strongly robust. For these reasons, we employ empirical Bayes estimators of correlation in place of their Bayesian counterparts. We show how to construct two different kinds of simultaneous Bayes correlation estimators: the first assumes no apriori correlation between baseline parameters; the second assumes a common unknown correlation between them. Estimators of the latter type frequently have significantly smaller MSE than those of the former type which, in turn, frequently have significantly smaller MSE than their Pearson estimator counterparts. For purposes of illustrating our results, we examine the problem of inferring the relationships between gene expression level vectors, in the context of observing microarray experimental data.     

On the Dynamics of Unemployment and Wage Distributions

Postel‐Vinay and Robin's (2002) sequential auction model is extended to allow for aggregate productivity shocks. Workers exhibit permanent differences in ability while firms are identical. Negative aggregate productivity shocks induce job destruction by driving the surplus of matches with low ability workers to negative values. Endogenous job destruction coupled with worker heterogeneity thus provides a mechanism for amplifying productivity shocks that offers an original solution to the unemployment volatility puzzle (Shimer (2005)). Moreover, positive or negative shocks may lead employers and employees to renegotiate low wages up and high wages down when agents' individual surpluses become negative. The model delivers rich business cycle dynamics of wage distributions and explains why both low wages and high wages are more procyclical than wages in the middle of the distribution.     

The Contribution of Irish Immigrants to the Quebec (Canada) Gene Pool: An Estimation Using Data from Deep-Rooted Genealogies

European settlement in Quebec (Canada) began in the early 17th century, with the arrival of French pioneers. After the British Conquest in 1760, immigrants from the British Isles began to settle in some parts of Quebec. Many of these immigrants were Irish Catholics. Historians and genealogists have identified several names of Irish origin in the French Canadian population, and many scholars have wondered about the importance of the integration of Irish migrants and their descendants within this population. The purposes of this study are to identify and characterize the founders of Irish origin to estimate the importance of their genetic contribution to the contemporary Quebec population, and to measure the variability of this contribution according to the founders’ period of arrival and county of origin in Ireland. Data was obtained from a set of 2,223 ascending genealogies going back as far as the early 17th century. The average genealogical depth is a little more than 9 generations, with many branches reaching 16 or 17 generations. Although Irish founders explain less than 1% of the total Quebec gene pool, results show that nearly 21% of the genealogies contain at least one Irish founder. These founders contributed to the peopling of all regions of Quebec, but there are some important variations from one region to another. A majority of the Irish founders immigrated during the 19th century, and most of them came from the counties of Southern Ireland.     

A comparison of dependence function estimators in multivariate extremes

Various nonparametric and parametric estimators of extremal dependence have been proposed in the literature. Nonparametric methods commonly suffer from the curse of dimensionality and have been mostly implemented in extreme-value studies up to three dimensions, whereas parametric models can tackle higher-dimensional settings. In this paper, we assess, through a vast and systematic simulation study, the performance of classical and recently proposed estimators in multivariate settings. In particular, we first investigate the performance of nonparametric methods and then compare them with classical parametric approaches under symmetric and asymmetric dependence structures within the commonly used logistic family. We also explore two different ways to make nonparametric estimators satisfy the necessary dependence function shape constraints, finding a general improvement in estimator performance either (i) by substituting the estimator with its greatest convex minorant, developing a computational tool to implement this method for dimensions \(D\ge 2\) or (ii) by projecting the estimator onto a subspace of dependence functions satisfying such constraints and taking advantage of Bernstein–Bézier polynomials. Implementing the convex minorant method leads to better estimator performance as the dimensionality increases.     

Ethics in Operations Research and Management Sciences: A never-ending effort to combine rigor and passion

From practice to theory, we introduce a state-of-the-art stream of papers that promotes an inclusive and complementary consideration of both analytical methods and ethical values in Operations Research and Management Sciences (OR/MS). We suggest a perspective according to which the consideration of ethics in OR/MS constitutes an enrichment of our discipline as well as a contribution to a more sustainable future in general.     

Loss of Life Caused by the Flooding of New Orleans After Hurricane Katrina: Analysis of the Relationship Between Flood Characteristics and Mortality

In this article a preliminary analysis of the loss of life caused by Hurricane Katrina in the New Orleans metropolitan area is presented. The hurricane caused more than 1,100 fatalities in the state of Louisiana. A preliminary data set that gives information on the recovery locations and individual characteristics for 771 fatalities has been analyzed. One-third of the analyzed fatalities occurred outside the flooded areas or in hospitals and shelters in the flooded area. These fatalities were due to the adverse public health situation that developed after the floods. Two-thirds of the analyzed fatalities were most likely associated with the direct physical impacts of the flood and mostly caused by drowning. The majority of victims were elderly: nearly 60% of fatalities were over 65 years old. Similar to historical flood events, mortality rates were highest in areas near severe breaches and in areas with large water depths. An empirical relationship has been derived between the water depth and mortality and this has been compared with similar mortality functions proposed based on data for other flood events. The overall mortality among the exposed population for this event was approximately 1%, which is similar to findings for historical flood events. Despite the fact that the presented results are preliminary they give important insights into the determinants of loss of life and the relationship between mortality and flood characteristics.