Nonlinear IV Panel Unit Root Tests

This paper presents the nonlinear IV methodology as an effective inferential basis for nonstationary panels. The nonlinear IV method resolves the inferential difficulties in testing for unit roots arising from the intrinsic heterogeneities and cross-dependencies of panel models. Individual units are allowed to be dependent through correlations among innovations, interrelatedness of short-run dynamics and/or cross-sectional cointegrations. If based on the instrumental variables that are nonlinear transformations of the lagged levels, the usual IV estimation of the augmented Dickey-Fuller type regressions yields asymptotically normal unit root tests for panels with general dependencies and heterogeneities. Moreover, the nonlinear IV estimation allows for the use of covariates to further increase power, and order statistics to test for more flexible forms of hypotheses, which are especially important in heterogeneous panels.

Estimating heritability in plant breeding programs

Heritability is an important notion in, e.g., human genetics, animal breeding and plant breeding, since the focus of these fields lies on the relationship between phenotypes and genotypes. A phenotype is the composite of an organisms observable traits, which is determined by its underlying genotype, by environmental factors and by genotype-environment interactions. For a set of genotypes, the notion of heritability expresses the proportion of the phenotypic variance that is attributable to the genotypic variance. Furthermore, as it is an intraclass correlation, heritability can also be interpreted as, e.g., the squared correlation between phenotypic and genotypic values. It is important to note that heritability was originally proposed in the context of animal breeding where it is the individual animal that represents the basic unit of observation. This stands in contrast to plant breeding, where multiple observations for the same genotype are obtained in replicated trials. Furthermore, trials are usually conducted as multi-environment trials (MET), where an environment denotes a year × location combination and represents a random sample from a target population of environments. Hence, the observations for each genotype first need to be aggregated in order to obtain a single phenotypic value, which is usually done by obtaining some sort of mean value across trials and replicates. As a consequence, heritability in the context of plant breeding is referred to as heritability on an entry-mean basis and its standard estimation method is a linear combination of variances and trial dimensions. Ultimately, I find that there are two main uses for heritability in plant breeding: The first is to predict the response to selection and the second is as a descriptive measure for the usefulness and precision of cultivar trials. Heritability on an entry-mean basis is suited for both purposes as long as three main assumptions hold: (i) the trial design is completely balanced/orthogonal, (ii) genotypic effects are independent and (iii) variances and covariances are constant. In the last decades, however, many advancements in the methodology of experimental design for and statistical analysis of plant breeding trials took place. As a consequence it is seldom the case that all three of above mentioned assumptions are met. Instead, the application of linear mixed models enables the breeder to straightforwardly analyze unbalanced data with complex variance structures. Chapter 2 exemplarily demonstrates some of the flexibility and benefit of the mixed model framework for typically unbalanced MET by using a bivariate mixed model analyses to jointly analyze two MET for cultivar evaluation, which differ in multiple crucial aspects such as plot size, trial design and general purpose. Such an approach can lead to higher accuracy and precision of the analysis and thus more efficient and successful breeding programs. It is not clear, however, how to define and estimate a generalized heritability on an entry-mean basis for such settings. Therefore, multiple alternative methods for the estimation of heritability on an entry-mean basis have been proposed. In Chapter 3, six alternative methods are applied to four typically unbalanced MET for cultivar evaluation and compared to the standard method. The outcome suggests that the standard method over-estimates heritability, while all of the alternative methods show similar, lower estimates and thus seem able to handle this kind of unbalanced data. Finally, it is argued in Chapter 4 that heritability in plant breeding is not actually based on or aiming at entry-means, but on the differences between them. Moreover, an estimation method for this new proposal of heritability on an entry-difference basis (H_Delta^2/h_Delta^2) is derived and discussed, as well as exemplified and compared to other methods via analyzing four different datasets for cultivar evaluation which differ in their complexity. I argue that regarding the use of heritability as a descriptive measure, H_Delta^2/h_Delta^2, can on the one hand give a more detailed and meaningful insight than all other heritability methods and on the other hand reduces to other methods under certain circumstances. When it comes to the use of heritability as a means to predict the response to selection, the outcome of this work discourages this as a whole. Instead, response to selection should be simulated directly and thus without using any ad hoc heritability measure.In der Humangenetik, Tier- und Pflanzenzüchtung sowie anderen Forschungsdisziplinen, bei denen die Beziehung zwischen Genotypen und Phänotypen im Fokus steht, ist die Heritabilität eine wichtige Maßzahl. Der Phänotyp setzt sich aus einem oder mehreren beobachteten Merkmalen eines Organismus zusammen und wird durch den zugrunde liegenden Genotypen, durch Umwelteinflüsse, sowie durch Genotyp-Umwelt-Wechselwirkungseffekte bestimmt. Die Heritabilität gibt an, welcher Anteil der phänotypischen Varianz genetisch bedingt ist. Sie kann als quadrierte Korrelation zwischen phänotypischen und genotypischen Werte interpretiert werden. Ursprünglich wurde die Heritabilität in der Tierzüchtung vorgeschlagen, in welcher das einzelne Tier die kleinste Beobachtungseinheit darstellt. Dies steht im Gegensatz zur Pflanzenzüchtung, in der meist wiederholte Versuche durchgeführt werden, so dass derselbe Genotyp reproduziert und mehrfach beobachtet werden kann. Hinzu kommt, dass die Versuche meist in Versuchsserien an mehreren Standorten und über mehrere Jahre hinweg durchgeführt werden. Um also einen phänotypischen Wert je Genotyp zu erhalten, müssen dessen Beobachtungen aggregiert werden, was meist durch eine Form von Mittelwertbildung geschieht. Aus diesem Grund wird Heritabilität in der Pflanzenzüchtung standardmäßig als Heritabilität auf Sortenmittelwertbasis. Ich sehe zwei Hauptnutzen von Heritabilität in der Pflanzenzüchtung: Zum einen kann mit ihr der Selektionserfolg vorhergesagt werden und zum anderen dient sie als beschreibende Maßzahl für die Präzision und Brauchbarkeit eines Versuchs. Die Heritabilität auf Sortenmittelwertbasis ist für beide Zwecke geeignet solange folgende Bedingungen erfüllt sind: (i) Das Versuchsdesign ist vollkommen balanciert/orthogonal, (ii) die Genotyp-Effekte sind unabhängig und (iii) alle Varianzen, sowie Kovarianzen sind konstant. In den letzten Jahrzehnten gab es mehrere Weiterentwicklungen in der Methodik des Versuchsdesigns sowie der statistischen Analyse von Pflanzenzüchtungsversuchen. Gemischte Modelle ermöglichen komplexe Varianzstrukturen und unbalancierte Daten auszuwerten In Kapitel 2 wird beispielhaft gezeigt, welche Möglichkeiten und Vorteile in der Anwendung von gemischten Modellen liegen, indem typisch unbalancierte Datensätze von zwei verschiedenen Sortenversuchsserien mithilfe eines bivariaten gemischten Modells gemeinsam ausgewertet werden. Ansätze wie dieser können eine höhere Analyseexaktheit und präzision erzielen und demnach die Effizienz und den Erfolg von Pflanzenzüchtungsprogrammen steigern. Gleichzeitig führt dies dazu, dass die oben genannten Bedingungen nur selten erfüllt sind. In solchen Fällen ist dann nicht klar, wie eine Heritabilität auf Sortenmittelwertbasis definiert und geschätzt werden kann. Mehrere alternative Methoden wurden vorgeschlagen. In Kapitel 3 werden sechs dieser alternativen Methoden für vier typische Datensätze aus Sortenversuchsserien berechnet und miteinander, sowie mit der Standardmethode verglichen. Die Ergebnisse deuten darauf hin, dass letztere die Heritabilität überschätzt, während alle alternativen Methoden ähnliche, niedrigere Schätzungen zeigen. Dies lässt vermuten, dass diese Methoden besser für die vorliegenden, unbalancierten Daten geeignet sind. Abschließend wird in Kapitel 4 gezeigt, dass Heritabilität in der Pflanzenzüchtung im Grunde nicht auf Genotypmittelwerten sondern auf deren Differenzen basiert. Hieraus wird eine Methode zur Berechnung einer generalisierten Heritabilität auf Sortendifferenzbasis (H_Delta^2/h_Delta^2) hergeleitet und diskutiert. Vier unterschiedlich komplexe Datensätze von Sortenversuchen werden verwendet und mit alternativen Heritabilitätsschätzern verglichen. Bezüglich der Verwendung der Heritabilität als beschreibende Maßzahl bietet H_Delta^2/h_Delta^2 einen ausführlicheren und bedeutsameren Einblick als die alternativen Heritabilitätsschätzer oder die Standardmethode. Hinzu kommt, dass H_Delta^2/h_Delta^2 die bisher bekannten Methoden nicht nur verallgemeinert, sondern in Spezialfällen exakt abbildet. Basierend auf den Resultaten der gesamten Arbeit rate ich von der Verwendung von Heritabilität als Mittel zur Vorhersage des Selektionserfolges ab. Der Selektionserfolg sollte stattdessen direkt simuliert werden, sodass die Nutzung einer ad hoc Schätzungsmethode der Heritabilität unnötig ist

Compression of thick laminated composite beams with initial impact-like damage

While the study of compression after impact of laminated composites has been under consideration for many years, the complexity of the damage initiated by low velocity impact has not lent itself to simple predictive models for compression strength. The damage modes due to non-penetrating, low velocity impact by large diameter objects can be simulated using quasi-static three-point bending. The resulting damage modes are less coupled and more easily characterized than actual impact damage modes. This study includes the compression testing of specimens with well documented initial damage states obtained from three-point bend testing. Compression strengths and failure modes were obtained for quasi-isotropic stacking sequences from 0.24 to 1.1 inches thick with both grouped and interspersed ply stacking. Initial damage prior to compression testing was divided into four classifications based on the type, extent, and location of the damage. These classifications are multiple through-thickness delaminations, isolated delamination, damage near the surface, and matrix cracks. Specimens from each classification were compared to specimens tested without initial damage in order to determine the effects of the initial damage on the final compression strength and failure modes. A finite element analysis was used to aid in the understanding and explanation of the experimental results

Diabetic peripheral neuropathy class prediction by multicategory support vector machine model: a cross- sectional study

OBJECTIVES Diabetes is increasing in worldwide prevalence, toward epidemic levels. Diabetic neuropathy, one of the most common complications of diabetes mellitus, is a serious condition that can lead to amputation. This study used a multicategory support vector machine (MSVM) to predict diabetic peripheral neuropathy severity classified into four categories using patients’ demographic characteristics and clinical features. METHODS In this study, the data were collected at the Diabetes Center of Hamadan in Iran. Patients were enrolled by the convenience sampling method. Six hundred patients were recruited. After obtaining informed consent, a questionnaire collecting general information and a neuropathy disability score (NDS) questionnaire were administered. The NDS was used to classify the severity of the disease. We used MSVM with both one-against-all and one-against-one methods and three kernel functions, radial basis function (RBF), linear, and polynomial, to predict the class of disease with an unbalanced dataset. The synthetic minority class oversampling technique algorithm was used to improve model performance. To compare the performance of the models, the mean of accuracy was used. RESULTS For predicting diabetic neuropathy, a classifier built from a balanced dataset and the RBF kernel function with a one-against-one strategy predicted the class to which a patient belonged with about 76% accuracy. CONCLUSIONS The results of this study indicate that, in terms of overall classification accuracy, the MSVM model based on a balanced dataset can be useful for predicting the severity of diabetic neuropathy, and it should be further investigated for the prediction of other diseases

Fiscal Adjustment and the Costs of Public Debt Service: Evidence from OECD Countries

We use a panel of 21 OECD countries from 1970 to 2009 to investigate the effects of different fiscal adjustment strategies on long-term interest rates – a key fiscal indicator reflecting the costs of government debt service. A government confronted with high deficits and rising debt will sooner or later need to enact fiscal adjustments in order to avoid solvency problems. Over the last four decades, such measures taken by governments in OECD countries have varied in duration, size, composition and in their success to re-establish fiscal sustainability. Controlling for various economic, fiscal and political factors, we find that the size and the composition of a fiscal adjustment significantly affect interest rates as well as yield spreads. Adjustments that are relatively large and those that primarily depend on expenditure cuts lead to substantially lower long-term interest rates. However, periods of fiscal adjustments do not generally have an influence on interest rates, even if they were successful and led to lower deficits and debt levels. Instead, financial markets only seem to value strict and decisive measures – a clear sign that the government’s pledge to cut the deficit is credible.fiscal adjustment, consolidation policy, government debt, deficit, interest rates