Publikationen

Financial returns exhibit common behavior described at best by factor models, but also fat tails, which may be captured by α-stable distributions. This paper concentrates on estimating factor models with multivariate α-stable distributed and independent factors and idiosyncratic noises under the assumption of time constant distribution (static factor models) or time-varying conditional distribution (GARCH factor models). While the simulation from such a distribution is straightforward, the estimation encounters difficulties. These difficulties are overcome in this paper by implementing the indirect inference estimation method with the multivariate Studentâ s t as the auxiliary distribution.

Are people blamed for being pivotal if they implement an unpopular outcome in a sequential voting process? We conduct an experimental voting game and analyze how pivotality affects responsibility attribution by parties who can be negatively affected by the voting outcome. We measure responsibility attribution by assigned punishment points and find that pivotal decision makers are blamed significantly more than non-pivotal decision makers. Moreover, we find that some voters avoid being pivotal by voting strategically to delegate the pivotal vote to subsequent decision makers.

Has there been an increase in positive assortative mating? Does assortative mating contribute to household income inequality? Data from the United States Census Bureau suggests there has been a rise in assortative mating. Additionally, assortative mating a¤ects household income inequality. In particular, if matching in 2005 between husbands and wives had been random, instead of the pattern observed in the data, then the Gini coe¢ cient would have fallen from the observed 0.43 to 0.34, so that income inequality would be smaller. Thus, assortative mating is important for income inequality. The high level of married female labor-force participation in 2005 is important for this result.

Are people blamed for being pivotal if they implement an unpopular outcome in a sequential voting process? We conduct an experimental voting game and analyze how pivotality affects responsibility attribution by parties who can be negatively affected by the voting outcome. We measure responsibility attribution by assigned punishment points and find that pivotal decision makers are blamed significantly more than non-pivotal decision makers. Moreover, we find that some voters avoid being pivotal by voting strategically to delegate the pivotal vote to subsequent decision makers.

The study analyses the combined influence of obesity and lifestyle behaviors on health care utilization and health care costs. Therefore I analyze the interaction of obesity, nutrition and physical activity based on a community level dataset from a German city. In addition to the expected convex effects of age and chronic diseases for utilization, the results indicate that BMI and physical inactivity have an independent influence on G.P. visits as well as for hospitalization. The key finding of the cost analysis is that health care costs increase in consequence of a completely sedentary lifestyle by 505 € independent of the individual’s BMI level. The results also confirm that compared to individuals of normal weight, the medical costs of the group of overweight people (by 377 €) and the group of obese people (by 565 €) are significantly increased. Even without significant weight reductions public programs against a sedentary lifestyle can be a way to reduce health care spending, and thus a sole focus on weight reduction might underestimate the additional benefits of changes in lifestyle behaviors.

We derive a framework for asymptotically valid inference in stable vector autoregressive (VAR) models with conditional heteroskedasticity of unknown form. We prove a joint central limit theorem for the VAR slope parameter and innovation covariance parameter estimators and address bootstrap inference as well. Our results are important for correct inference on VAR statistics that depend both on the VAR slope and the variance parameters as e.g. in structural impulse response functions (IRFs). We also show that wild and pairwise bootstrap schemes fail in the presence of conditional heteroskedasticity if inference on (functions) of the unconditional variance parameters is of interest because they do not correctly replicate the relevant fourth moments' structure of the error terms. In contrast, the residual-based moving block bootstrap results in asymptotically valid inference. We illustrate the practical implications of our theoretical results by providing simulation evidence on the finite sample properties of different inference methods for IRFs. Our results point out that estimation uncertainty may increase dramatically in the presence of conditional heteroskedasticity. Moreover, most inference methods are likely to understate the true estimation uncertainty substantially in finite samples.