Bayesian inference for generalized additive mixed models based on Markov random field priors

Most regression problems in practice require flexible semiparametric forms of the predictor for modelling the dependence of responses on covariates. Moreover, it is often necessary to add random effects accounting for overdispersion caused by unobserved heterogeneity or for correlation in longitudinal or spatial data. We present a unified approach for Bayesian inference via Markov chain Monte Carlo simulation in generalized additive and semiparametric mixed models. Different types of covariates, such as the usual covariates with fixed effects, metrical covariates with non-linear effects, unstructured random effects, trend and seasonal components in longitudinal data and spatial covariates, are all treated within the same general framework by assigning appropriate Markov random field priors with different forms and degrees of smoothness. We applied the approach in several case-studies and consulting cases, showing that the methods are also computationally feasible in problems with many covariates and large data sets. In this paper, we choose two typical applications.     

Derivation of the theoretical autocovariance and autocorrelation function of autoregessive moving average processes

Closed form expressions for the theoretical autocovariance and autocorrelation function of mixed autoregressive moving average processes are presented. The results provide insight into the construction of autocovariances and autocorrelatians and are useful in theoretical analysis, model identification as well as in implementing maximum likelihood estimation algorithms.     

Affine LIBOR models driven by real-valued affine processes

The class of affine LIBOR models is appealing since it satisfies three central requirements of interest rate modeling. It is arbitrage-free, interest rates are nonnegative, and caplet and swaption prices can be calculated analytically. In order to guarantee nonnegative interest rates affine LIBOR models are driven by nonnegative affine processes, a restriction that makes it hard to produce volatility smiles. We modify the affine LIBOR models in such a way that real-valued affine processes can be used without destroying the nonnegativity of interest rates. Numerical examples show that in this class of models, pronounced volatility smiles are possible.     

Multiple hypothesis tests based On conditional differences in means

Many hypothesis tests are univariate tests and cannot cope with multiple hypothesis without an auxiliary procedure as e. g. the Bonferroni-Holm-procedure. At the same time, there is an urgent need for testing multiple hypothesis due to the very simple existing methods as the Bonferroni-correction or the Bonferroni-Holm-procedure, which suffers from a very small local significance level to detect statistical inferences or the drawback that logical and statistical dependencies among the test statistics are not used, whereby its detection is NP-hard. In honour of this occasion, we present a multiple hypothesis test for i.i.d. random variables based on conditional differences in means, which is capable to cope with multiple hypothesis and does not suffer on such drawbacks as the Bonferroni-correction or the Bonferroni-Holm-procedure. Thereby, the computation time can be neglected.     

L'effet des obstacles aux échanges sur l'emploi généré par les chaînes d'approvisionnement mondiales

Les auteurs étudient l'effet des obstacles aux échanges internationaux de biens et de services sur les marchés du travail des pays producteurs en partant des estimations du BIT sur l'emploi imputable aux chaînes d'approvisionnement mondiales. L'analyse empirique confirme largement les prédictions d'un modèle théorique calibré avec des données de la base WIOD (2000 et 2011). Les obstacles au commerce de produits manufacturés ou de services ont un effet transfrontalier, intrasectoriel et intersectoriel, sur l'emploi, qui reflète l'interdépendance croissante entre pays et secteurs. L'effet intersectoriel s'affirme avec le temps. Les politiques commerciales semblent donc pouvoir avoir des effets externes importants sur les marchés du travail étrangers.     

Empirical Prediction Intervals for County Population Forecasts

Population forecasts entail a significant amount of uncertainty, especially for long-range horizons and for places with small or rapidly changing populations. This uncertainty can be dealt with by presenting a range of projections or by developing statistical prediction intervals. The latter can be based on models that incorporate the stochastic nature of the forecasting process, on empirical analyses of past forecast errors, or on a combination of the two. In this article, we develop and test prediction intervals based on empirical analyses of past forecast errors for counties in the United States. Using decennial census data from 1900 to 2000, we apply trend extrapolation techniques to develop a set of county population forecasts; calculate forecast errors by comparing forecasts to subsequent census counts; and use the distribution of errors to construct empirical prediction intervals. We find that empirically-based prediction intervals provide reasonably accurate predictions of the precision of population forecasts, but provide little guidance regarding their tendency to be too high or too low. We believe the construction of empirically-based prediction intervals will help users of small-area population forecasts measure and evaluate the uncertainty inherent in population forecasts and plan more effectively for the future.     

Childlessness and Psychological Well-Being in Midlife and Old Age: An Examination of Parental Status Effects Across a Range of Outcomes

The study explores and distinguishes links between parental status (childless persons, parents with residential children, and empty nest parents) and a range of psychological well-being outcomes in midlife and old age. Data are from the first wave of the Norwegian Life Course, Ageing and Generation (NorLAG) study (n = 5,189). We separate outcomes into cognitive (life satisfaction and self-esteem) and affective (positive and negative affect, depression, loneliness) components. Parental status has a net effect on cognitive well-being among women, as childless women report significantly lower life satisfaction and self-esteem than both mothers with residential children and empty nest mothers. However, motherhood is inconsequential for affective well-being. Among men, parental status is unrelated to any of the well-being aspects. Parental status effects are not modified by age, marital status, and education. The results demonstrate the importance of investigating the effect of parental status and other objective circumstances on a range of psychological well-being outcomes. Furthermore, the results reviewed and presented indicate somewhat more positive effects of parenthood in the Nordic countries than in the US, highlighting the role of social policies in shaping the impact of parental status on well-being.     

Interkulturelles Coaching – eine komplexe,systemische Herausforderung

Intercultural coaching takes place in the highly complex reality of a globalized world. The coachee is an individual acting in the situational context of his assignment and his corporate culture. He has been socialized in his own culture, but is communicating and interacting with people who have been socialized in different cultures. In order to identify and solve problems, all three aspects (person, situation, culture) have to be considered. Some aspects may have more influence than others may, but they combine in a closely interwoven system. The authors outline the theoretical background of intercultural coaching and illustrate the process with case studies.