Suchergebnisse

Geschichtsbücher dokumentieren und transportieren wie kaum eine andere Quelle nationale Identität. Vor diesem Hintergrund nimmt Christian Weiß in seinem Buch Lehrwerke für deutsche und französische Volksschulen in den Blick. Er bleibt nicht beim üblichen Vergleich der Inhalte stehen, sondern analysiert, ausgehend von Klassikern der Geschichts- und Erzähltheorie, die den Texten zugrunde liegenden narrativen Strukturen. Es zeigt sich, dass die Ähnlichkeiten im nationalen Selbstverständnis in den Lehrwerken tatsächlich nur an der Oberfläche zu finden sind. Der Nationalismus, den deutsche und französische Geschichtsbücher beförderten, basierte auf grundsätzlich unterschiedlichen Vorstellungen von der Nation, ihrer Vergänglichkeit sowie ihrer historischen und künftigen Rolle in der Welt.

Viele große Projekte von Unternehmen und Kommunen stoßen auf Widerstand der Bürger. Diese möchten mitreden und mitbestimmen, wenn es um die Veränderung ihres Umfeldes geht. Was auf den ersten Blick als störend wahrgenommen wird, hat aber für Unternehmen und Kommunen Vorteile: Zum einen erhalten sie kostenloses Expertenwissen der Bürger, zum anderen können durch eine frühe Beteiligung der Bürger schnell Widerstände aus der Bevölkerung überwunden und so sogar Planungen beschleunigt werden. Gleichzeitig wird damit Vertrauen und Transparenz geschaffen, was zu einem positiven Image von Unternehmen und Politik bzw. Kommune beiträgt.
Companies and municipalities working on big projects often have to face resistance by the citizens. These want to have a say, too, and to participate when it comes to changes in their environment. On first sight, this might be thought of as an interference. But there are also advantages for companies and municipalities. First, they profit from the free expertise of the citizens. Second, by letting the citizens participate at an early stage possible resistances can be overcome and plannings might even be accelerated. At the same time, this leads to trust and transparency. This in turn contributes to a positive image of companies and politics respectively municipalities.
Keywords: widerstände, projekte, planen, bürger, beteiligen

The analysis and modeling of categorical time series requires quantifying the extent of dispersion and serial dependence. The dispersion of categorical data is commonly measured by Gini index or entropy, but also the recently proposed extropy measure can be used for this purpose. Regarding signed serial dependence in categorical time series, we consider three types of κ-measures. By analyzing bias properties, it is shown that always one of the κ-measures is related to one of the above-mentioned dispersion measures. For doing statistical inference based on the sample versions of these dispersion and dependence measures, knowledge on their distribution is required. Therefore, we study the asymptotic distributions and bias corrections of the considered dispersion and dependence measures, and we investigate the finite-sample performance of the resulting asymptotic approximations with simulations. The application of the measures is illustrated with real-data examples from politics, economics and biology.

The analysis and modeling of categorical time series requires quantifying the extent of dispersion and serial dependence. The dispersion of categorical data is commonly measured by Gini index or entropy, but also the recently proposed extropy measure can be used for this purpose. Regarding signed serial dependence in categorical time series, we consider three types of κ-measures. By analyzing bias properties, it is shown that always one of the κ-measures is related to one of the above-mentioned dispersion measures. For doing statistical inference based on the sample versions of these dispersion and dependence measures, knowledge on their distribution is required. Therefore, we study the asymptotic distributions and bias corrections of the considered dispersion and dependence measures, and we investigate the finite-sample performance of the resulting asymptotic approximations with simulations. The application of the measures is illustrated with real-data examples from politics, economics and biology.

Forecast error is not only caused by the randomness of the data‐generating process but also by the uncertainty due to estimated model parameters. We investigate these different sources of forecast error for a popular type of count process, the Poisson first‐order integer‐valued autoregressive (INAR(1)) process. However, many of our analytical derivations also hold for the more general family of conditional linear AR(1) (CLAR(1)) processes. In addition, results from a simulation study are presented, to verify and complement our asymptotic approximations.

A statistical test for the degree of overdispersion of count data time series based on the empirical version of the (Poisson) index of dispersion is considered. The test design relies on asymptotic properties of this index of dispersion, which in turn have been analyzed for time series stemming from a compound Poisson (Poisson‐stopped sum) INAR(1) model. This approach is extended to the popular Poisson INARCH(1) model, which exhibits unconditional overdispersion but has an (equidispersed) conditional Poisson distribution. The asymptotic distribution of the index of dispersion if applied to time series stemming from such a model is derived. These results allow us to investigate the ability of the dispersion test to discriminate between Poisson INAR(1) and INARCH(1) models. Furthermore, the question is considered if the index of dispersion could be used to test the null of a Poisson INARCH(1) model against the alternative of an INARCH(1) model with additional conditional overdispersion.