Die Risikohomeostasistheorie (RHT) ist ein formales Konzept zur Erklärung menschlichen Verhaltens im Straßenverkehr bei verändertem Unfallrisiko. Vor dem Hintergrund des gegenwärtigen Standes der Ökonometrie weisen die Untersuchungen zur RHT mittels langer Zeitreihen einige Schwächen auf. Im folgenden wird versucht, diese Schwächen einerseits mit dem Stationaritätskonzept der Ökonometrie und andererseits mit einer auf Dummyvariablen basierenden Methode zu beheben. Gleichzeitig wird die Theorie einem neuerlichen Test auf ihre Gültigkeit hinsichtlich der Unfallsituation im Straßenverkehr in Deutschland unterzogen. Die Arbeit nimmt Bezug erstens auf die Wirksamkeit von Regulierungsmaßnahmen (hier: Verschärfung der Gurtanlegepflicht) und zweitens auf die Wirkungen der deutschen Wiedervereinigung. Beiden Ereignissen wird nach der RHT keine Wirkung zugesprochen. Die Ergebnisse der Analysen unterstützen die Thesen der RHT nur schwach. Sie belegen, daß konsequente und mit Strafandrohung belegte Regulierungsmaßnahmen entgegen dem Postulat der Risikohomeostasisthese eine stark positive Wirkung auf die Unfallsituation besitzen. Außerdem werden die komplexen Entscheidungsprozesse von Verkehrsteilnehmern im Kontext mehrerer theoretischer Konzepte untersucht. Es zeigt sich, das Theorien zur Beschreibung individuellen Verhaltens unter Unsicherheit sehr gut geeignet sind, tatsächliches Verhalten von Verkehrsteilnehmern zu erklären. / Risk homeostasis theory (RHT) is a behavioural theory of risk taking in road traffic. So far, most of the published papers concerning RHT and long time series are based on econometric methods which are not very well suited for this purpose. We propose here to address the issue using instead the econometric concept of stationarity and a concept based on dummy variables. We then test the RHT with German traffic accident data and specifically analyze compulsory traffic safety measures (the penalty for not using seat belts) as well as the effects of German reunification. Both are ineffective according to RHT. Our results, found by using several risk measures, show only weak evidence for RHT. Contrary to RHT, we can show that compulsory safety measures combined with penalties had a strict positive effect on the road traffic accident risk. We also develop a solution which focuses on the complex decision-making process of an individual in road traffic. This is done within the context of several theories explaining individuals decision-making under uncertainty. There we can show that these theoretical concepts are very well suited to explain actual behavior of road users.

This thesis consists of four essays that study the effect of demand and supply shocks (namely exchange rate shocks, policy shocks and oil price shocks) on the German automobile industry using time series analysis techniques; i.a. intervention analysis, (time-varying) VAR models, state space modeling and Kalman filtering. Chapter 2 investigates the degree of (EUR/USD) exchange rate susceptibility of the German automobile industry (and mechanical engineering industry). It is shown that – in contrast to well-known warnings by business representatives and politicians alike – an appreciation of the Euro does not necessarily cause German vehicle producing companies “pain” in the sense of an aggravated business climate (although the (quantity) effects on exports are as predicted by theory). Chapter 3 first of all deals with the effect of the global economic crisis on the German manufacturing sector in general and the German automobile industry in particular. Even more important, the influence of the scrapping scheme, that was introduced as part of the “Konjunkturpaket II” in 2009, is researched. Chapter 4 focusses on the role of (systematic) monetary policy as well as the automobile industry in the transmission of oil price shocks to the economy. Chapter 5 extends the results of chapter 4 by examining carefully the role of fuel-efficiency in explaining the different degrees of sensitivity of the vehicle industries in Germany, Japan and the US with regard to oil price shocks.

In der vorliegenden Dissertation wird eine Beschreibung der Phasendynamik irregulärer Oszillationen und deren Wechselwirkungen vorgestellt. Hierbei werden chaotische und stochastische Oszillationen autonomer dissipativer Systeme betrachtet. Für eine Phasenbeschreibung stochastischer Oszillationen müssen zum einen unterschiedliche Werte der Phase zueinander in Beziehung gesetzt werden, um ihre Dynamik unabhängig von der gewählten Parametrisierung der Oszillation beschreiben zu können. Zum anderen müssen für stochastische und chaotische Oszillationen diejenigen Systemzustände identifiziert werden, die sich in der gleichen Phase befinden. Im Rahmen dieser Dissertation werden die Werte der Phase über eine gemittelte Phasengeschwindigkeitsfunktion miteinander in Beziehung gesetzt. Für stochastische Oszillationen sind jedoch verschiedene Definitionen der mittleren Geschwindigkeit möglich. Um die Unterschiede der Geschwindigkeitsdefinitionen besser zu verstehen, werden auf ihrer Basis effektive deterministische Modelle der Oszillationen konstruiert. Hierbei zeigt sich, dass die Modelle unterschiedliche Oszillationseigenschaften, wie z. B. die mittlere Frequenz oder die invariante Wahrscheinlichkeitsverteilung, nachahmen. Je nach Anwendung stellt die effektive Phasengeschwindigkeitsfunktion eines speziellen Modells eine zweckmäßige Phasenbeziehung her. Wie anhand einfacher Beispiele erklärt wird, kann so die Theorie der effektiven Phasendynamik auch kontinuierlich und pulsartig wechselwirkende stochastische Oszillationen beschreiben. Weiterhin wird ein Kriterium für die invariante Identifikation von Zuständen gleicher Phase irregulärer Oszillationen zu sogenannten generalisierten Isophasen beschrieben: Die Zustände einer solchen Isophase sollen in ihrer dynamischen Entwicklung ununterscheidbar werden. Für stochastische Oszillationen wird dieses Kriterium in einem mittleren Sinne interpretiert. Wie anhand von Beispielen demonstriert wird, lassen sich so verschiedene Typen stochastischer Oszillationen in einheitlicher Weise auf eine stochastische Phasendynamik reduzieren. Mit Hilfe eines numerischen Algorithmus zur Schätzung der Isophasen aus Daten wird die Anwendbarkeit der Theorie anhand eines Signals regelmäßiger Atmung gezeigt. Weiterhin zeigt sich, dass das Kriterium der Phasenidentifikation für chaotische Oszillationen nur approximativ erfüllt werden kann. Anhand des Rössleroszillators wird der tiefgreifende Zusammenhang zwischen approximativen Isophasen, chaotischer Phasendiffusion und instabilen periodischen Orbits dargelegt. Gemeinsam ermöglichen die Theorien der effektiven Phasendynamik und der generalisierten Isophasen eine umfassende und einheitliche Phasenbeschreibung irregulärer Oszillationen. / Many natural systems embedded in a complex surrounding show irregular oscillatory dynamics. The oscillations can be parameterized by a phase variable in order to obtain a simplified theoretical description of the dynamics. Importantly, a phase description can be easily extended to describe the interactions of the system with its surrounding. It is desirable to define an invariant phase that is independent of the observable or the arbitrary parameterization, in order to make, for example, the phase characteristics obtained from different experiments comparable. In this thesis, we present an invariant phase description of irregular oscillations and their interactions with the surrounding. The description is applicable to stochastic and chaotic irregular oscillations of autonomous dissipative systems. For this it is necessary to interrelate different phase values in order to allow for a parameterization-independent phase definition. On the other hand, a criterion is needed, that invariantly identifies the system states that are in the same phase. To allow for a parameterization-independent definition of phase, we interrelate different phase values by the phase velocity. However, the treatment of stochastic oscillations is complicated by the fact that different definitions of average velocity are possible. For a better understanding of their differences, we analyse effective deterministic phase models of the oscillations based upon the different velocity definitions. Dependent on the application, a certain effective velocity is suitable for a parameterization-independent phase description. In this way, continuous as well pulse-like interactions of stochastic oscillations can be described, as it is demonstrated with simple examples. On the other hand, an invariant criterion of identification is proposed that generalizes the concept of standard (Winfree) isophases. System states of the same phase are identified to belong to the same generalized isophase using the following invariant criterion: All states of an isophase shall become indistinguishable in the course of time. The criterion is interpreted in an average sense for stochastic oscillations. It allows for a unified treatment of different types of stochastic oscillations. Using a numerical estimation algorithm of isophases, the applicability of the theory is demonstrated by a signal of regular human respiration. For chaotic oscillations, generalized isophases can only be obtained up to a certain approximation. The intimate relationship between these approximate isophase, chaotic phase diffusion, and unstable periodic orbits is explained with the example of the chaotic roes oscillator. Together, the concept of generalized isophases and the effective phase theory allow for a unified, and invariant phase description of stochastic and chaotic irregular oscillations.

Katholische Universiẗat, Diss.--Eichstätt-Ingolstadt, 2006.

Universiẗat, Diss.--Köln, 2007.

Tex., Rice Univ., Diss.--Houston, 2003. / Kopie, ersch. im Verl. UMI, Ann Arbor, Mich. - Enth. 3 Beitr.

Univ., Diss.--Basel, 2007.

Univ., Diss.--Konstanz, 2003. / Literaturverz. S. [273] - 283.

The use of Normalized Difference Vegetation Index (NDVI) time series over the last decades has increased our understanding of vegetation change dynamics from global to regional scale through quantitative analysis of inter-annual trends in NDVI and climatological parameters (rainfall and temperature). Change in land cover induced by human activities such as livestock grazing and deforestation for large-scale farming (subsistence and mechanized) has influenced the ecological pattern of the Guinea savannah region (GSR) of Nigeria, thereby resulting in loss of biodiversity and changes in vegetation cover. In the context of the GSR of Nigeria where agriculture still plays a major role in people’s economy, it is important to identify the relationship between climatic variables, vegetation productivity and human activities which can be used to understand the on-going transition processes. This study, therefore, examines the spatial and temporal relationship between NDVI and climate parameters, land use land cover change (LULCC) and the perspective of local people on vegetation change dynamics in the study region. In order to do this, bi-monthly NDVI3g time series datasets from Global Inventory Modeling and Mapping Studies (GIMMS), monthly rainfall datasets from Tropical Applications of Meteorology Satellite (TAMSAT), monthly temperature datasets from Climate Research Unit (CRU), national land use land cover (LULC) data of Nigeria from Forestry Management Evaluation & Coordination Unit (FORMECU), global land cover datasets from European Space Agency, Landsat imagery and socio-economic field data collection were used in order to understand vegetation change dynamics across the Guinea savannah regions of Nigeria. Time series analysis (TSA) was applied to both NDVI and climate data used in order to examine the temporal dynamics of vegetation cover change and to detect NDVI-climate relationship during the period from 1983 through 2011. Both parametric and non-parametric statistical models were employed for the assessment of long-term inter-annual trend on the decomposed time series datasets for the whole region (Guinea savannah region) and selected locations. In addition to the TSA, harmonic regression analysis was performed on NDVI and rainfall datasets in order to examine change in seasonality and phyto-phenological characteristics of vegetation. Detection of change in land use and land cover was done by extracting information from existing land cover datasets (ancillary datasets). CLASlite was used for the assessment of the extent of deforestation, while linkage between remotely sensed data and social science was carried out via field surveys based on questionnaires in order to understand the drivers of vegetation change. The study reveals that about 90 % of the Guinea savannah region show positive NDVI trends which indicate greening over time, while about 10 % of the region shows negative trends. This greening trends are closely related to regions where intensive agriculture is being practiced (also along inland valleys) while regions with negative trends show significant loss in woodlands (forest and shrublands) as well as herbaceous vegetation cover due to over-grazing by agro-pastoralism. The result confirms that there is a good relationship (statistically significant positive correlation) between rainfall and NDVI both on intra-annual and inter annual time scale for some selected locations in the study region (> 65 %), while negative statistical correlation exists between NDVI and temperature in the selected locations. This implies that vegetation growth (productivity) in the region is highly dependent on rainfall. The result of the harmonic regression analysis reveals a shift in the seasonal NDVI pattern, indicating an earlier start and a more prolonged growing season in 2011 than in 1983. This study proves significant change in LULC with evidence of an increase in the spatial extent of agricultural land (+ 30 %) and loss of woodlands (- 55 %) between 2000 and 2009 for Kogi State. The results of the socio-economic analysis (people’s perception) highlight that vegetation change dynamics in the study region are the resultant effects of increased anthropogenic activities rather than climatic variability. This study couples data from remote sensing and ground survey (socio-economics) for a better understanding of greening trend phenomena across the Guinea savannah region of Nigeria, thus filling the gap of inadequate information on environmental condition and human perturbation which is essential for proper land use management and vegetation monitoring.

Anhand von Temperaturdaten des Standortes Berlin-Tegel wird der Temperaturverlauf für einen Zeitraum von 15 Jahren modelliert. Es werden zwei verschiedene Regressionsansätze gegenübergestellt. Zuerst werden die Daten anhand einer Zeitreihenanalyse geschätzt. Dieses parametrische Verfahren wird mit der nicht-parametrischen Methode des Shape Invariant Modeling verglichen. Beiden Ansätzen liegt die Methode der Kleinsten Fehlerquadrate zugrunde. Die Zeitreihenanalyse als Spezialgebiet der Regressionsanalyse wird mit Hilfe einer Fourierreihe realisiert, um den periodischen Verlauf der Funktion abbilden zu können. Beim Shape Invariant Model werden mithilfe einer Referenzkurve die individuellen Regressionsfunktionen ermittelt. Als Referenz dient ein Basistag. Die Parameterschätzer werden durch Achsentransformation ermittelt.