This study offers an account of the Present Perfect and its usage in worldwide varieties of English. It is based on corpus data from the International Corpus of English (ICE), which is assessed both quantitatively and qualitatively. It follows up on two broad desiderata in Present Perfect research, namely (i) to extend the knowledge of this grammatical area beyond the traditional British/American English paradigm, and (ii) to provide a systematic account of language-external effects such as modes of discourse, macro-genres and text categories.
Occurrences of the Present Perfect are automatically extracted from part-of-speech tagged corpus files. Representative samples of the occurrences are then manually annotated for various factors (such as semantics, Aktionsart, temporal adverbials, sentence type, preceding tense, etc.) so that the distributions and the relative importance of these factors can be analyzed. In addition, the impact of alternative (non-standard) surface forms that may express a Present Perfect notion is considered.
Measures of similarity between the various varieties of English under investigation are established (e.g. with the help of multidimensional aggregational methods such as cluster analyses and phylogenetic networks) and findings are related to a number of general models of World Englishes, whose descriptive adequacy for this particular area of grammar is tested.
Using classical inference, hypothesis tests and confidence intervals are often based on large-sample assumptions, which are said to hold if the sample size is large enough.
The weakness of this approach is, that the researcher does not know what sample size is required for this purpose in a concrete situation.
A common problem that encounters in statistics is the procedure of modeling the relationship between explanatory variables and a binary response. Here logistic regression analysis often represents the appropriate method. This method is used to estimate the probability or odds of occurrence of the binary response in dependence of explanatory variables. But, what is the sample size to be large enough to base statistical conclusions on asymptotic properties?
The type of convergence, with which we are dealing here, is convergence in law, in the following denoted as L-convergence. If the limiting distribution of a statistic is continuous, then L-convergence is equivalent to convergence with respect to the Kolmogorov distance.
Therefore, the Kolmogorov distance is an effective tool for discussing the behavior of L-convergence.
The present work uses an autogenerated process that involves the classical theory of logistic regression analysis to explore the behavior of L-convergence by means of the Kolmogorov distance. Based on the Kolmogorov distance two methods are developed in order to investigate the behavior of L-convergence and its impacts on statistical conclusions. The first serves to extend the spectrum of methods to discuss the impacts of the Firth-penalization, the second to use the classical inference
as a more deliberate method with respect to asymptotic properties.
The first method consists of the distance-sample-size-diagram and the accuracy-diagram. The distance-sample-size-diagram represents the mean approximate Kolmogorov distance as a function of the predefined sample size. The predefined sample size is displayed on the horizontal axis and the mean approximate Kolmogorov distance between the statistic of interest and its limiting distribution on the vertical axis. This is a fruitful graphical representation of the behavior of L-convergence in dependence of the rate at which empirical information accrues. Finally the accuracy-diagram presents the actual accuracy function of a confidence interval and its reference derived from asymptotics. This diagram complements the distance-sample-size-diagram as a tool to study the impact of penalizations.
The second method, the p-value-uniform-diagram, shows the actual empirical cumulative distribution function of the p-values of a statical test and the cumulative distribution function of the uniform distribution as the reference of the former. A deviation from this reference indicates that L-convergence is not reached.
A fully Bayesian analysis of seasonal and nonseasonal forms of nonstationarity is presented. The thesis consists of three parts, which are structured as separate research articles. In the first paper a Bayesian approach to model selection in testing regressions for a zero frequency unit root with multiple structural breaks is proposed. For this purpose the number of breaks, the corresponding break dates as well as the number of autoregressive lags are treated as model indicators, whose posterior distributions are computed using a hybrid Markov chain Monte Carlo (MCMC) approach that allows to generate random draws from parameter spaces of varying dimension. The second part of the thesis is devoted to seasonal forms of nonstationarity. Here a Bayesian testing approach for a periodic unit root in the presence of a break at unknown time for quarterly and monthly data is presented and the required posterior distribution is derived.
In addition, a Bayesian F-test is suggested to test for seasonal and nonseasonal unit roots again controlling for a possible break. Instead of resorting to a model selection approach by choosing one particular model specification for testing, a Bayesian model averaging (BMA) approach is proposed to capture the model uncertainty associated with a specic parametrization of the test regression. In the third part of the thesis a Bayesian periodic autoregressive (PAR) model is then utilized for the prediction of quarterly and monthly time series data. A model averaging prediction approach for PAR models of unknown lag orders, number of breaks and break dates is proposed in order to improve the forecasting accuracy compared to conditional approaches. Further the joint posterior distribution of the multistep ahead forecasts is derived and an MCMC approach, based on data augmentation, is presented to generate random draws from the marginal posterior predictive distributions. In each of the three articles a Monte Carlo study is conducted to analyze the presented methods under different data generating processes.
In the first two parts the presented testing approaches are utilized to examine if there is empirical evidence for persistence or hysteresis in the annual unemployment rates of OECD countries.
In the last part of the thesis it is demonstrated how the suggested BMA prediction approach can improve forecasting accuracy compared to conditional, i.e. model selected, Bayesian PAR models using unadjusted monthly unemployment rates of East- and West-Germany and of the 16 German federal states.
In Germany, private retirement provision is a topic of increasing importance. The ageing population in combination with unemployment, an increasing number of temporary work contracts, more individuals who work part-time or in jobs not subject to social insurance contributions, make it problematic to finance the pensions of the retired in a pay-as-you-go financed pension system. Besides, the number of individuals not able to acquire sufficient pension claims exceeding the needs-oriented basic pension is increasing. Several pension reforms have taken place in order to alleviate the pressure on the pay-as-you-go system. In 2001, a voluntarily funded part was introduced to close the pension gap which has slowly been rising due to a declining replacement rate in the statutory pension system. Individuals now have to decide if they start to provide for retirement privately, how much they are going to save and where to invest. Such decisions require a sound knowledge of the German pension system and general financial knowledge in order to be able to approximate retirement needs and to compare financial products.
Based on the theory of saving and its behavioral refinements, a decision model has been developed in this thesis which describes each step from thinking about retirement to actually saving for retirement. Each of these steps has been investigated empirically in order to find out more about the hurdles individuals face on their way towards private retirement savings.
Based on an instrumental variable estimation, it will be shown that providing information about retirement provision alone will not be sufficient to make people think about an appropriate retirement income, to induce people to make concrete retirement plans and to increase the number of individuals who translate their plans into action. Instead of providing general pension knowledge, offering concrete and targeted information at the time it is needed might be a more successful strategy. These idea, results and conclusions stem from Oehler and Wilhelm-Oehler 2009a, 2011 who analyze the data from „Altersvorsorge macht Schule” („retirement planning goes school”). They recommend a practice-oriented, case-based financial education as well as a „meta education” to improve the „meta literacy” as shown by Oehler (2004, 2009a, 2011, 2012a, 2012d-e, 2013a-b). „Meta literacy” in this sense means that it is more important to know methods or people who can solve the problem when it appears, than acquiring all knowledge themselves being prepared to solve all possible problems (Oehler/Wilhelm-Oehler 2009, 2011; Oehler 2011, 2012a, 2012d-e, 2013a-b).
This strategy may also be successful to solve the problem of time constraints which many individuals stated to be the main reason why they would not participate in a retirement seminar. Furthermore, the confidence in one’s own knowledge seems to be more important than actual knowledge which requires measures to increase consumer confidence. Such a measure could be, for example, a hypothetical situation in which seminar participants have to evaluate the offer they received from a financial advisor (Oehler 2004, 2005b, 2006, 2011, 2012a, 2012d-e, 2013a-b).
According to the literature findings in the last five decades it is known that individuals fall back to heuristics in order to simplify decision. This behavior has also been observed in this work. Even though they own a pension product, they stated they did not try to figure out how much retirement wealth would be necessary to live an adequate retirement life. Hence, they must have followed some kind of decision rule to decide, among others, about the amount they save. Using heuristics was more prevalent among individuals owning a “Riester Pension” than among individuals owning a company pension. Since individuals with a company pension often receive information about the pension plan through the employer or via employer sponsored retirement seminars, such seminars seem likely to have the potential of increasing the number of individuals who engage in retirement planning before starting to save.
The second one is that individuals who admit that they tend to procrastinate on financial decisions are more likely to join a retirement seminar than individuals who indicate that they would rather not procrastinate. Making people aware of the widespread problem of procrastinating retirement savings might increase the number of individuals who realize that they have procrastinated retirement planning and henceforth increase the number of individuals participating in retirement seminars.
The current cultural transition of our society into a digital society influences all aspects of human life. New technologies like the Internet and mobile devices enable an unobstructed access to knowledge in worldwide networks. These advancements bring with them a great freedom in decisions and actions of individuals but also a growing demand for an appropriate mastering of this freedom of choice and the amount of knowledge that has become available today. Naturally, this observable rise and progress of new technologies—gently but emphatically becoming part of people’s everyday lives—not only changes the way people work, communicate, and shape their leisure but also the way people learn.
This thesis is dedicated to an examination of how learners can meet these requirements with the support that modern technology is able to provide to learners. More precisely, this thesis places a particular emphasis that is absent from previous work in the field and thus makes it distinctive: the explicit focus on individual learners. As a result, the main concern of this thesis can be described as the examination, development, and implementation of personal information management in learning. Altogether two different steps towards a solution have been chosen: the development of a theoretical framework and its practical implementation into a comprehensive concept.
To establish a theoretical framework for personal information management in learning, the spheres of learning, e-learning, and personalised learning have been combined with theories of organisational and personal knowledge management to form a so far unique holistic view of personal information management in learning. The development of this framework involves the identification of characteristics, needs, and challenges that distinguish individual learners from within the larger crowd of uniform learners.
The theoretical framework defined within the first part is transferred to a comprehensive technical concept for personal information management in learning. The realisation and design of this concept as well as its practical implementation are strongly characterised by the utilisation of information retrieval techniques to support individual learners. The characteristic feature of the resulting system is a flexible architecture that enables the unified acquisition, representation, and organisation of information related to an individual’s learning and supports an improved find-ability of personal information across all relevant sources of information.
The most important results of this thesis have been validated by a comparison with current projects in related areas and within a user study.