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Die Dissertation beschäftigt sich mit einer neuartigen Art von Branch-and-Bound Algorithmen, deren Unterschied zu klassischen Branch-and-Bound Algorithmen darin besteht, dass
das Branching durch die Addition von nicht-negativen Straftermen zur Zielfunktion erfolgt
anstatt durch das Hinzufügen weiterer Nebenbedingungen. Die Arbeit zeigt die theoretische Korrektheit des Algorithmusprinzips für verschiedene allgemeine Klassen von Problemen und evaluiert die Methode für verschiedene konkrete Problemklassen. Für diese Problemklassen, genauer Monotone und Nicht-Monotone Gemischtganzzahlige Lineare Komplementaritätsprobleme und Gemischtganzzahlige Lineare Probleme, präsentiert die Arbeit
verschiedene problemspezifische Verbesserungsmöglichkeiten und evaluiert diese numerisch.
Weiterhin vergleicht die Arbeit die neue Methode mit verschiedenen Benchmark-Methoden
mit größtenteils guten Ergebnissen und gibt einen Ausblick auf weitere Anwendungsgebiete
und zu beantwortende Forschungsfragen.
Survey data can be viewed as incomplete or partially missing from a variety of perspectives and there are different ways of dealing with this kind of data in the prediction and the estimation of economic quantities. In this thesis, we present two selected research contexts in which the prediction or estimation of economic quantities is examined under incomplete survey data.
These contexts are first the investigation of composite estimators in the German Microcensus (Chapters 3 and 4) and second extensions of multivariate Fay-Herriot (MFH) models (Chapters 5 and 6), which are applied to small area problems.
Composite estimators are estimation methods that take into account the sample overlap in rotating panel surveys such as the German Microcensus in order to stabilise the estimation of the statistics of interest (e.g. employment statistics). Due to the partial sample overlaps, information from previous samples is only available for some of the respondents, so the data are partially missing.
MFH models are model-based estimation methods that work with aggregated survey data in order to obtain more precise estimation results for small area problems compared to classical estimation methods. In these models, several variables of interest are modelled simultaneously. The survey estimates of these variables, which are used as input in the MFH models, are often partially missing. If the domains of interest are not explicitly accounted for in a sampling design, the sizes of the samples allocated to them can, by chance, be small. As a result, it can happen that either no estimates can be calculated at all or that the estimated values are not published by statistical offices because their variances are too large.
Do Personality Traits, Trust and Fairness Shape the Stock-Investing Decisions of an Individual?
(2023)
This thesis is comprised of three projects, all of which are fundamentally connected to the choices that individuals make about stock investments. Differences in stock market participation (SMP) across countries are large and difficult to explain. The second chapter focuses on differences between Germany (low SMP) and East Asian countries (mostly high SMP). The study hypothesis is that cultural differences regarding social preferences and attitudes towards inequality lead to different attitudes towards stock markets and subsequently to different SMPs. Using a large-scale survey, it is found that these factors can, indeed, explain a substantial amount of the country differences that other known factors (financial literacy, risk preferences, etc.) could not. This suggests that social preferences should be given a more central role in programs that aim to enhance SMP in countries like Germany. The third chapter documented the importance of trust as well as herding for stock ownership decisions. The findings show that trust as a general concept has no significant contribution to stock investment intention. A thorough examination of general trust elements reveals that in group and out-group trust have an impact on individual stock market investment. Higher out group trust directly influences a person's decision to invest in stocks, whereas higher in-group trust increases herding attitudes in stock investment decisions and thus can potentially increase the likelihood of stock investments as well. The last chapter investigates the significance of personality traits in stock investing and home bias in portfolio selection. Findings show that personality traits do indeed have a significant impact on stock investment and portfolio allocation decisions. Despite the fact that the magnitude and significance of characteristics differ between two groups of investors, inexperienced and experienced, conscientiousness and neuroticism play an important role in stock investments and preferences. Moreover, high conscientiousness scores increase stock investment desire and portfolio allocation to risky assets like stocks, discouraging home bias in asset allocation. Regarding neuroticism, a higher-level increases home bias in portfolio selection and decreases willingness to stock investment and portfolio share. Finally, when an investor has no prior experience with portfolio selection, patriotism generates home bias. For experienced investors, having a low neuroticism score and a high conscientiousness and openness score seemed to be a constant factor in deciding to invest in a well-diversified international portfolio
This cumulative thesis encompass three studies focusing on the Weddell Sea region in the Antarctic. The first study produces and evaluates a high quality data set of wind measurements for this region. The second study produces and evaluates a 15 year regional climate simulation for the Weddell Sea region. And the third study produces and evaluates a climatology of low level jets (LLJs) from the simulation data set. The evaluations were done in the attached three publications and the produced data sets are published online.
In 2015/2016, the RV Polarstern undertook an Antarctic expedition in the Weddell Sea. We operated a Doppler wind lidar on board during that time running different scan patterns. The resulting data was evaluated, corrected, processed and we derived horizontal wind speed and directions for vertical profiles with up to 2 km height. The measurements cover 38 days with a temporal resolution of 10-15 minutes. A comparisons with other radio sounding data showed only minor differences.
The resulting data set was used alongside other measurements to evaluate temperature and wind of simulation data. The simulation data was produced with the regional climate model CCLM for the period of 2002 to 2016 for the Weddell Sea region. Only smaller biases were found except for a strong warm bias during winter near the surface of the Antarctic Plateau. Thus we adapted the model setup and were able to remove the bias in a second simulation.
This new simulation data was then used to derive a climatology of low level jets (LLJs). Statistics of occurrence frequency, height and wind speed of LLJs for the Weddell Sea region are presented along other parameters. Another evaluation with measurements was also performed in the last study.