Testing the Granger Noncausality Hypothesis in Stationary Nonlinear Models of Unknown Functional Form

In this article, we propose a general method for testing the Granger noncausality hypothesis in stationary nonlinear models of unknown functional form. These tests are based on a Taylor expansion of the nonlinear model around a given point in the sample space. We study the performance of our tests by a Monte Carlo experiment and compare these to the most widely used linear test. Our tests appear to be well-sized and have reasonably good power properties.     

Friends or foes? Migrants and sub-state nationalists in Europe

ABSTRACT

How do sub-state nationalists respond to the growing presence of cultural diversity in their ‘homelands’ resulting from migration? Sub-state nationalists in Europe, in ‘nations without states’ such as Catalonia and Scotland, have been challenging the traditional nation-state model for many decades. While the arguments in favour of autonomy or independence levelled by these movements have become more complex, sub-state nationalist movements remain grounded by their perceived national community that is distinct from the majority nation. Migration to the ‘homeland’ of a sub-state nation, then, presents a conundrum for sub-state elites that we label the ‘legitimation paradox’: too much internal diversity may undermine the claim to cultural distinctiveness. We engage with three common intervening variables thought to influence how sub-state nationalists confront the ‘legitimation paradox’: civic/ethnic nationalism, degree of political autonomy, and party competition. Our overarching argument is that none of these factors have a unidirectional or determinate effect on the sub-state nationalism-immigration nexus, which is why the nuanced case studies that comprise this Special Issue are worthwhile endeavours.     

Simulating space-time random fields with nonseparable Gneiting-type covariance functions

Statistics and Computing - Two algorithms are proposed to simulate space-time Gaussian random fields with a covariance function belonging to an extended Gneiting class, the definition of which...     

Model averaging inconcentration–QT analyses

This article describes how a frequentist model averaging approach can be used for concentration–QT analyses in the context of thorough QTc studies. Based on simulations, we have concluded that starting from three candidate model families (linear, exponential, and Emax) the model averaging approach leads to treatment effect estimates that are quite robust with respect to the control of the type I error in nearly all simulated scenarios; in particular, with the model averaging approach, the type I error appears less sensitive to model misspecification than the widely used linear model. We noticed also few differences in terms of performance between the model averaging approach and the more classical model selection approach, but we believe that, despite both can be recommended in practice, the model averaging approach can be more appealing because of some deficiencies of model selection approach pointed out in the literature. We think that a model averaging or model selection approach should be systematically considered for conducting concentration–QT analyses. Copyright © 2016 John Wiley & Sons, Ltd.     

Comparison of hazard rates for dependent random variables

In this paper, we define and study a new notion for the comparison of the hazard rates of two random variables taking into account their mutual dependence. Properties, applications and the comparison for a data set are given.     

Testing Hypotheses in Nonparametric Models of Production

Data envelopment analysis (DEA) and free disposal hull (FDH) estimators are widely used to estimate efficiency of production. Practitioners use DEA estimators far more frequently than FDH estimators, implicitly assuming that production sets are convex. Moreover, use of the constant returns to scale (CRS) version of the DEA estimator requires an assumption of CRS. Although bootstrap methods have been developed for making inference about the efficiencies of individual units, until now no methods exist for making consistent inference about differences in mean efficiency across groups of producers or for testing hypotheses about model structure such as returns to scale or convexity of the production set. We use central limit theorem results from our previous work to develop additional theoretical results permitting consistent tests of model structure and provide Monte Carlo evidence on the performance of the tests in terms of size and power. In addition, the variable returns to scale version of the DEA estimator is proved to attain the faster convergence rate of the CRS-DEA estimator under CRS. Using a sample of U.S. commercial banks, we test and reject convexity of the production set, calling into question results from numerous banking studies that have imposed convexity assumptions. Supplementary materials for this article are available online.     

Conditional VAR and Expected Shortfall: A New Functional Approach

We estimate two well-known risk measures, the value-at-risk (VAR) and the expected shortfall, conditionally to a functional variable (i.e., a random variable valued in some semi(pseudo)-metric space). We use nonparametric kernel estimation for constructing estimators of these quantities, under general dependence conditions. Theoretical properties are stated whereas practical aspects are illustrated on simulated data: nonlinear functional and GARCH(1,1) models. Some ideas on bandwidth selection using bootstrap are introduced. Finally, an empirical example is given through data of the S&P 500 time series.     

A nonparametric hypothesis test for heteroscedasticity

In this paper, a hypothesis test for heteroscedasticity is proposed in a nonparametric regression model. The test statistic, which uses the residuals from a nonparametric fit of the mean function, is based on an adaptation of the well-known Levene's test. Using the recent theory for analysis of variance when the number of factor levels goes to infinity, the asymptotic distribution of the test statistic is established under the null hypothesis of homocedasticity and under local alternatives. Simulations suggest that the proposed test performs well in several situations, especially when the variance is a nonlinear function of the predictor.     

Fatigue management by truck drivers in real life situations: some suggestions to improve training

Truck driver fatigue is a major safety issue for truck drivers and the public in general. Although training prepares drivers to effectively operate a truck, it tends to minimize the importance of working constraints faced daily on-the-job and thus reduces its impact on safety and effectiveness. With experience, drivers develop skills to combat fatigue. Documenting these skills can contribute to improved training of apprentices. An ethnographic approach was used to better understand the real-life fatigue management skills of truck drivers. Participant observation was used to analyze the activity of apprentices in training and the activity of truck drivers at work. Observations indicated that training focused on time management and regulations, but did not prepare trainees to manage real-life constraints. Experienced drivers were not merely managing time; rather they were managing working constraints (including time) as a whole. To do so, they used two strategies: managing psycho-physical transformations and dynamic work planning. By integrating psycho-physical preoccupations into all aspects of work and by preparing future drivers to face real-life constraints, we could better train and prepare apprentices. Drivers do develop effective skills to combat fatigue which can improve training and better prepare future drivers to face daily constraints. These improvements can have a significant impact on fatigue and safety in the transportation industry.     

Gender differences in the relationship between intimate partner violence victimization and the perception of dating situations among college students

Although the prevalence and severity of dating violence among college students is well known, the relationship between past victimization and perceptions of future dating situations has not been examined. Using both qualitative and quantitative research methods, this study investigated gender differences in the relationship between intimate partner violence victimization and the perceptions of dating situations. The study found that the more psychological, physical, or sexual violence that was experienced by females, the more likely they perceived dating situations as inappropriate. Males, on the other hand, were more likely to report aggressive behaviors in dating situations only if victimized by sexual violence. Implications for professionals working with college students or community prevention programs are discussed.