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This dissertation focuses on the link between labour market institutions and precautionary savings. It is evaluated whether private households react to changes in social insurance provision such as the income replacement in case of unemployment by increased savings for precautionary reasons. The dissertation consists of three self-contained chapters, each focusing on slightly different aspects of the topic. The first chapter titled "Precautionary saving and the (in)stability of subjective earnings uncertainty" empirically looks at the influence of future income uncertainty on household saving behavior. Numerous cross-section studies on precautionary saving use subjective expectations regarding the income variance one year ahead as a proxy for income uncertainty. Using such proxies observed only at one point in time, however, may give rise to biased estimates for precautionary wealth if expectations are not stable over time. Survey data from the Dutch DNB Household Survey suggest that subjective future income distributions are not stable over the mid-term. Moreover, in this study I contrast estimates of precautionary wealth using the variation coefficient observed at one point in time with those using a simple mid-term average. Estimates of precautionary wealth based on the average are about 40% to 80% higher than the estimates using the variation coefficient observed only once. In addition to that, wealth accumulation for precautionary reasons is estimated for different parts of the income distribution. The share of precautionary wealth is highest for households at the center of the income distribution. By linking saving behaviour with unemployment insurance, the following chapters then shed some light on an issue that has largely been neglected in the literature on labour market institutions so far. Whereas the third chapter models the relevance of unemployment insurance for income uncertainty and intertemporal decision making during institutional reform processes, chapter 4 seeks to establish empirically a relationship between saving behavior and unemployment insurance. Social insurance, especially unemployment insurance, provides agents with income insurance against not marketable income risks. Since the early 1990s, reform measures like more activating policies as suggested by the OECD Jobs Study in 1994 have been observed in Europe. In the third chapter it is argued that such changes in unemployment insurance reduce public insurance and increase income uncertainty. Moreover, a simple three period model is discussed which shows a link between a welfare state reform and agents' saving decisions as one possible reaction of agents to self-insure against income risk. Two sources of uncertainty seem to be important in this context: (1) uncertain results of the reform process concerning the replacement rate, and (2) uncertainty regarding the timing of information about the content of the reform. It can be shown that the precautionary motive for saving explains an increased accumulation of capital in times of reform activities. In addition to that, early information about the expected replacement rate increases agents' utility and reduces under and oversaving. Following the argument of the previous chapters, that an important feature of labour market institutions in modern welfare states is to provide cash transfers as income replacement in case of unemployment, it is hypothesised that unemployment benefits reduce the motive to save for precautionary reasons. Based on consumer sentiment data from the European Commission's Consumer Survey, chapter four finally provides some evidence that aggregate saving intentions are significantly influenced by unemployment benefits. It can be shown that higher benefits lower the intention to save.
Stress represents a significant problem for Western societies inducing costs as high as 3-4 % of the European gross national products, a burden that is continually increasing (WHO Briefing, EUR/04/5047810/B6). The classical stress response system is the hypothalamic-pituitary-adrenal (HPA) axis which acts to restore homeostasis after disturbances. Two major components within the HPA axis system are the glucocorticoid receptor (GR) and the mineralocorticoid receptor (MR). Cortisol, released from the adrenal glands at the end of the HPA axis, binds to MRs and with a 10 fold lower affinity to GRs. Both, impairment of the HPA axis and an imbalance in the MR/GR ratio enhances the risk for infection, inflammation and stress related psychiatric disorders. Major depressive disorder (MDD) is characterised by a variety of symptoms, however, one of the most consistent findings is the hyperactivity of the HPA axis. This may be the result of lower numbers or reduced activity of GRs and MRs. The GR gene consists of multiple alternative first exons resulting in different GR mRNA transcripts whereas for the MR only two first exons are known to date. Both, the human GR promoter 1F and the homologue rat Gr promoter 1.7 seem to be susceptible to methylation during stressful early life events resulting in lower 1F/1.7 transcript levels. It was proposed that this is due to methylation of a NGFI-A binding site in both, the rat promoter 1.7 and the human promoter 1F. The research presented in this thesis was undertaken to determine the differential expression and methylation patterns of GR and MR variants in multiple areas of the limbic brain system in the healthy and depressed human brain. Furthermore, the transcriptional control of the GR transcript 1F was investigated as expression changes of this transcript were associated with MDD, childhood abuse and early life stress. The role of NGFI-A and several other transcription factors on 1F regulation was studied in vitro and the effect of Ngfi-a overexpression on the rat Gr promoter 1.7 in vivo. The susceptibility to epigenetic programming of several GR promoters was investigated in MDD. In addition, changes in methylation levels have been determined in response to a single acute stressor in rodents. Our results showed that GR and MR first exon transcripts are differentially expressed in the human brain, but this is not due to epigenetic programming. We showed that NGFI-A has no effect on endogenous 1F/1.7 expression in vitro and in vivo. We provide evidence that the transcription factor E2F1 is a major element in the transcriptional complex necessary to drive the expression of GR 1F transcripts. In rats, highly individual methylation patterns in the paraventricular nucleus of the hypothalamus (PVN) suggest that this is not related to the stressor but can rather be interpreted as pre-existing differences. In contrast, the hippocampus showed a much more uniform epigenetic status, but still is susceptible to epigenetic modification even after a single acute stress suggesting a differential "state‟ versus "trait‟ regulation of the GR gene in different brain regions. The results of this thesis have given further insight in the complex transcriptional regulation of GR and MR first exons in health and disease. Epigenetic programming of GR promoters seems to be involved in early life stress and acute stress in adult rats; however, the susceptibility to methylation in response to stress seems to vary between brain regions.
The brain is the central coordinator of the human stress reaction. At the same time, peripheral endocrine and neural stress signals act on the brain modulating brain function. Here, three experimental studies are presented demonstrating this dual role of the brain in stress. Study I shows that centrally acting insulin, an important regulator of energy homeostasis, attenuates the stress related cortisol secretion. Studies II and III show that specific components of the stress reaction modulate learning and memory retrieval, two important aspects of higher-order brain function.
During pregnancy every eighth woman is treated with glucocorticoids. Glucocorticoids inhibit cell division but are assumed to accelerate the differentiation of cells. In this review animal models for the development of the human fetal and neonatal hypothalamic-pituitary-adrenal (HPA) axis are investigated. It is possible to show that during pregnancy in humans, as in most of the here-investigated animal models, a stress hyporesponsive period (SHRP) is present. In this period, the fetus is facing reduced glucocorticoid concentrations, by low or absent fetal glucocorticoid synthesis and by reduced exposure to maternal glucocorticoids. During that phase, sensitive maturational processes in the brain are assumed, which could be inhibited by high glucocorticoid concentrations. In the SHRP, species-specific maximal brain growth spurt and neurogenesis of the somatosensory cortex take place. The latter is critical for the development of social and communication skills and the secure attachment of mother and child. Glucocorticoid treatment during pregnancy needs to be further investigated especially during this vulnerable SHRP. The hypothalamus and the pituitary stimulate the adrenal glucocorticoid production. On the other hand, glucocorticoids can inhibit the synthesis of corticotropin-releasing hormone (CRH) in the hypothalamus and of adrenocorticotropic hormone (ACTH) in the pituitary. Alterations in this negative feedback are assumed among others in the development of fibromyalgia, diabetes and factors of the metabolic syndrome. In this work it is shown that the fetal cortisol surge at the end of gestation is at least partially due to reduced glucocorticoid negative feedback. It is also assumed that androgens are involved in the control of fetal glucocorticoid synthesis. Glucocorticoids seem to prevent masculinization of the female fetus by androgens during the sexual gonadal development. In this work a negative interaction of glucocorticoids and androgens is detectable.
This work is concerned with two kinds of objects: regular expressions and finite automata. These formalisms describe regular languages, i.e., sets of strings that share a comparatively simple structure. Such languages - and, in turn, expressions and automata - are used in the description of textual patterns, workflow and dependence modeling, or formal verification. Testing words for membership in any given such language can be implemented using a fixed - i.e., finite - amount of memory, which is conveyed by the phrasing finite-automaton. In this aspect they differ from more general classes, which require potentially unbound memory, but have the potential to model less regular, i.e., more involved, objects. Other than expressions and automata, there are several further formalisms to describe regular languages. These formalisms are all equivalent and conversions among them are well-known.However, expressions and automata are arguably the notions which are used most frequently: regular expressions come natural to humans in order to express patterns, while finite automata translate immediately to efficient data structures. This raises the interest in methods to translate among the two notions efficiently. In particular,the direction from expressions to automata, or from human input to machine representation, is of great practical relevance. Probably the most frequent application that involves regular expressions and finite automata is pattern matching in static text and streaming data. Common tools to locate instances of a pattern in a text are the grep application or its (many) derivatives, as well as awk, sed and lex. Notice that these programs accept slightly more general patterns, namely ''POSIX expressions''. Concerning streaming data, regular expressions are nowadays used to specify filter rules in routing hardware.These applications have in common that an input pattern is specified in form a regular expression while the execution applies a regular automaton. As it turns out, the effort that is necessary to describe a regular language, i.e., the size of the descriptor,varies with the chosen representation. For example, in the case of regular expressions and finite automata, it is rather easy to see that any regular expression can be converted to a finite automaton whose size is linear in that of the expression. For the converse direction, however, it is known that there are regular languages for which the size of the smallest describing expression is exponential in the size of the smallest describing automaton.This brings us to the subject at the core of the present work: we investigate conversions between expressions and automata and take a closer look at the properties that exert an influence on the relative sizes of these objects.We refer to the aspects involved with these consideration under the titular term of Relative Descriptional Complexity.
Extension of inexact Kleinman-Newton methods to a general monotonicity preserving convergence theory
(2011)
The thesis at hand considers inexact Newton methods in combination with algebraic Riccati equation. A monotone convergence behaviour is proven, which enables a non-local convergence. Above relation is transferred to a general convergence theory for inexact Newton methods securing the monotonicity of the iterates for convex or concave mappings. Several application prove the pratical benefits of the new developed theory.
Variational inequality problems constitute a common basis to investigate the theory and algorithms for many problems in mathematical physics, in economy as well as in natural and technical sciences. They appear in a variety of mathematical applications like convex programming, game theory and economic equilibrium problems, but also in fluid mechanics, physics of solid bodies and others. Many variational inequalities arising from applications are ill-posed. This means, for example, that the solution is not unique, or that small deviations in the data can cause large deviations in the solution. In such a situation, standard solution methods converge very slowly or even fail. In this case, so-called regularization methods are the methods of choice. They have the advantage that an ill-posed original problem is replaced by a sequence of well-posed auxiliary problems, which have better properties (like, e.g., a unique solution and a better conditionality). Moreover, a suitable choice of the regularization term can lead to unconstrained auxiliary problems that are even equivalent to optimization problems. The development and improvement of such methods are a focus of current research, in which we take part with this thesis. We suggest and investigate a logarithmic-quadratic proximal auxiliary problem (LQPAP) method that combines the advantages of the well-known proximal-point algorithm and the so-called auxiliary problem principle. Its exploration and convergence analysis is one of the main results in this work. The LQPAP method continues the recent developments of regularization methods. It includes different techniques presented in literature to improve the numerical stability: The logarithmic-quadratic distance function constitutes an interior point effect which allows to treat the auxiliary problems as unconstrained ones. Furthermore, outer operator approximations are considered. This simplifies the numerical solution of variational inequalities with multi-valued operators since, for example, bundle-techniques can be applied. With respect to the numerical practicability, inexact solutions of the auxiliary problems are allowed using a summable-error criterion that is easy to implement. As a further advantage of the logarithmic-quadratic distance we verify that it is self-concordant (in the sense of Nesterov/Nemirovskii). This motivates to apply the Newton method for the solution of the auxiliary problems. In the numerical part of the thesis the LQPAP method is applied to linearly constrained, differentiable and nondifferentiable convex optimization problems, as well as to nonsymmetric variational inequalities with co-coercive operators. It can often be observed that the sequence of iterates reaches the boundary of the feasible set before being close to an optimal solution. Against this background, we present the strategy of under-relaxation, which robustifies the LQPAP method. Furthermore, we compare the results with an appropriate method based on Bregman distances (BrPAP method). For differentiable, convex optimization problems we describe the implementation of the Newton method to solve the auxiliary problems and carry out different numerical experiments. For example, an adaptive choice of the initial regularization parameter and a combination of an Armijo and a self-concordance step size are evaluated. Test examples for nonsymmetric variational inequalities are hardly available in literature. Therefore, we present a geometric and an analytic approach to generate test examples with known solution(s). To solve the auxiliary problems in the case of nondifferentiable, convex optimization problems we apply the well-known bundle technique. The implementation is described in detail and the involved functions and sequences of parameters are discussed. As far as possible, our analysis is substantiated by new theoretical results. Furthermore, it is explained in detail how the bundle auxiliary problems are solved with a primal-dual interior point method. Such investigations have by now only been published for Bregman distances. The LQPAP bundle method is again applied to several test examples from literature. Thus, this thesis builds a bridge between theoretical and numerical investigations of solution methods for variational inequalities.
This thesis centers on formal tree languages and on their learnability by algorithmic methods in abstractions of several learning settings. After a general introduction, we present a survey of relevant definitions for the formal tree concept as well as special cases (strings) and refinements (multi-dimensional trees) thereof. In Chapter 3 we discuss the theoretical foundations of algorithmic learning in a specific type of setting of particular interest in the area of Grammatical Inference where the task consists in deriving a correct formal description for an unknown target language from various information sources (queries and/or finite samples) in a polynomial number of steps. We develop a parameterized meta-algorithm that incorporates several prominent learning algorithms from the literature in order to highlight the basic routines which regardless of the nature of the information sources have to be run through by all those algorithms alike. In this framework, the intended target descriptions are deterministic finite-state tree automata. We discuss the limited transferability of this approach to another class of descriptions, residual finite-state tree automata, for which we propose several learning algorithms as well. The learnable class by these techniques corresponds to the class of regular tree languages. In Chapter 4we outline a recent range of attempts in Grammatical Inference to extend the learnable language classes beyond regularity and even beyond context-freeness by techniques based on syntactic observations which can be subsumed under the term 'distributional learning', and we describe learning algorithms in several settings for the tree case taking this approach. We conclude with some general reflections on the notion of learning from structural information.
Psychiatric/Behavioral disorders/traits are usually polygenic in nature, where a particular phenotype is the manifestation of multiple genes. However, the existence of large families with numerous members who are affected by these disorders/traits steers us towards a Mendelian (or monogenic) possibility, where the phenotype is caused by a single gene. In order to better understand the genetic architecture of general psychiatric/behavioral disorders/traits, this thesis investigates large pedigrees that display a Mendelian pattern for attention-deficit/hyperactivity disorder, schizophrenia and bipolar disorder. Numerous challenges in the field of psychiatric and behavioral sciences have impeded the genetic investigation of such disorders/traits. Examples include frequent cross-disorders, genetic heterogeneity across subjects as well as the use of diagnostic tools that can be subjective at times. To overcome these challenges, this thesis investigates large multi-generational pedigrees, which comprise a significant number of members who exhibit specific psychiatric/behavioral phenotypes. These pedigrees provide high-resolution experimental setups that can dissect the genetic complexities of psychiatric/behavioral disorders/traits. This thesis adopts a classical two-stage genetic approach to investigate the various psychiatric/behavioral disorders/traits in large pedigrees. The classical two-stage genetic approach is commonly used by many human geneticists to study a wide spectrum of human physiological disorders but is only being applied to the field of psychiatric and behavioral genetics recently. Through the study of large pedigrees, this thesis discovers the genomic regions that may play a causative role in the expression of certain psychiatric/behavioral disorders/traits within the vast genome.
By rodent studies it has been shown that the mineralocorticoid receptor (MR) is a candidate gene for the investigation of cognitive functions comparable to human executive function. The present work addresses the question if polymorphisms in the MR gene can act as a "probe" to explain a part of the interindividual variance of human executive functions. For this purpose, 72 healthy young participants were assigned to four equally sized groups, concerning their particular MR genotype for two common MR polymorphisms. They were investigated in an electroencephalogram (EEG) test session, accomplishing two cognitive tests while delivering saliva samples for subsequent cortisol measures. The two tests chosen for the assessment of executive functions were the Attention Network Task (ANT) and a modified version of the Wisconsin Card Sorting Test (WCST).Chapter 1 of the present work reports of the rational bases for the empirical approach, which were built up on a broad theoretical background presented in Chapter 2. In the third chapter, the investigation and results of the statistical analysis for behavioral data (i.e. reaction times, accuracy/error rates) are presented. No association with MR polymorphisms was found for the reaction times of both tests. For the accuracy rate, differences between genotype groups were found for ANT and WCST, indicating an association of MR polymorphisms and accuracy in the Alertness and Executive Control network of the ANT and during the detection of an intradimensional shift in the WCST. Data acquisition and the results for EEG data analyses are presented in Chapter 4. The results show that groups differing for MR genotype show different activity over prefrontal motor areas during the process of answering to the ANT. Those group differences again were prominent for the Alertness and Executive Control network. A tendency for further significant group differences was found for activity on frontopolar positions in extradimensional rule switching. Chapter 5 summarizes the findings for the analysis of salivary free cortisol, showing a tendency for an association between MR polymorphisms and a mildly stimulated Hypothalamus-pituitary-adrenal (HPA) axis during the test situation. The results of the different measures are integrated and discussed in Chapter 6 within the scope of novel findings in investigating the functionality of the chosen MR polymorphisms. Finally, Chapter 7 gives an outlook on the methodology and constraints of future research strategies to further describe the role of the MR in human cognitive function.