01 Abstract Foreign exchange reserves are a useful tool and a buffer but maintaining an amount that is too large can be costly to the economy. Recent accumulation of these reserves points to the importance of this topic. This thesis focuses on one specific part of the effect of FX reserves on the economy - the inflation. I use panel data for 74 countries from the year 1996 to the year 2017. There is a certain degree of model uncertainty for which this thesis accounts for by using Bayesian model averaging (BMA) estimation technique. The findings from my model averaging estimations show FX reserves to not be of importance for inflation determination with close to no change when altering lags, variables, when limiting the sample to fixed FX regimes nor when limiting the sample to inflation targeting regimes. The most important variables are estimated to be a central bank financial strength proxy, exchange rate depreciation, money supply, inflation targeting, and capital account openness. These results are robust to lag changes, prior changes, and for the most part remain the same when Pooled OLS is used.

Valuation of used cars, affected by various technical attributes and information asymmetry, is the key objective of all agents operating on the automobile mar- ket. This thesis, focusing on a hedonic price analysis, aims to determine basic as well as additional attributes as determinants of a used car market price. In addition, the analysis sheds light upon novel attributes (service records, cigarette smoke pollution of a vehicle interior, selling channel factor in the e- commerce environment, and a German geographical division). The hedonic price research uses the unique data sample of the German used car market, extracted from the database of the e-commerce platform AutoScout24 com- prised of almost 51 thousand vehicles and 57 attributes. The model selection is specified by the incorporation of the Bayesian model averaging approach. The research proves the complexity of a valuation of a used vehicle in a term of a substantial number of relevant variables. The most interesting innovative conclusions are non-significant effect of selling channels and small local price differences among two German regions. Remarkable are also the significant effect of the status of previous owners, bodywork colour, and smoke pollution. The estimated vehicle lifespan of 10 years shows that cars have shorter than...

Islam and Economic Performance: A Meta-Analysis Patrik Kratochvíla June 28, 2021 Abstract The ongoing economic supremacy of the West has prompted debates on the ability of non-Christian religions to generate economic growth. The academic literature focusing on the Islamic religion o↵ers multiple answers, leaving the matter unresolved and with no definite conclusion. Based on a quantitative sur- vey of 315 estimates collected from 41 relevant academic studies, Islam exerts a positive and statistically significant e↵ect on economic growth in 40% of cases, a negative and statistically significant e↵ect in 10% of cases, and virtually zero e↵ect in 50% of cases. Tests for publication bias indicate slightly preferential reporting against negative estimates. When I correct for this bias, I find that the mean e↵ect of Islam on economic growth is positive but economically small. I also construct 79 moderator variables capturing methodological heterogeneity among the primary studies and apply the method of Bayesian model averaging to deal with model uncertainty in meta-analysis. The analysis shows that the heterogeneity in the results is primarily driven by di↵erences in the sample com- position and the choice of control variables, and to a lesser extent by estimation characteristics and proxies for Islam employed. 1

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Pharmacometrics is widely used in drug development. Models are developed to describe pharmacological measurements with data gathered from a clinical trial. The information can then be applied to, for instance, safely establish dose-response relationships of a substance. Glycated hemoglobin (HbA1c) is a common biomarker used by models within antihyperglycemic drug development, as it reflects the average plasma glucose level over the previous 8-12 weeks. There are five different nonlinear mixed-effects models that describes HbA1c-formation. They use different biomarkers such as mean plasma glucose (MPG), fasting plasma glucose (FPG), fasting plasma insulin (FPI) or a combination of those. The aim of this study was to compare their performances on a population and an individual level using model averaging (MA) and to explore if reduced trial durations and different treatment could affect the outcome. Multiple weighting methods were applied to the MA workflow, such as the Akaike information criterion (AIC), cross-validation (CV) and a bootstrap model averaging method. Results show that in general, models that use MPG to describe HbA1c-formation on a population level could potentially outperform models using other biomarkers, however, models have shown similar performance on individual level. Further studies on the relationship between biomarkers and model performances must be conducted, since it could potentially lay the ground for better individual HbA1c-predictions. It can then be applied in antihyperglycemic drug development and to possibly reduce sample sizes in a clinical trial. With this project, we have illustrated how to perform MA on the aforementioned models, using different biomarkers as well as the difference between model weights on a population and individual level.

This thesis consists of three chapters on economic and econometric applications of Bayesian parameter estimation and model comparison. The first two chapters study the incidental parameter problem mainly under a linear autoregressive (AR) panel data model with fixed effect. The first chapter investigates the problem from a model comparison perspective. The major finding in the first chapter is that consistency in parameter estimation and model selection are interrelated. The reparameterization of the fixed effect parameter proposed by Lancaster (2002) may not provide a valid solution to the incidental parameter problem if the wrong set of exogenous regressors are included. To estimate the model consistently and to measure its goodness of fit, the Bayes factor is found to be more preferable for model comparson than the Bayesian information criterion based on the biased maximum likelihood estimates. When the model uncertainty is substantial, Bayesian model averaging is recommended. The method is applied to study the relationship between financial development and economic growth. The second chapter proposes a correction function approach to solve the incidental parameter problem. It is discovered that the correction function exists for the linear AR panel model of order p when the model is stationary with strictly exogenous regressors. MCMC algorithms are developed for parameter estimation and to calculate the Bayes factor for model comparison. The last chapter studies how stock return's predictability and model uncertainty affect a rational buy-and-hold investor's decision to allocate her wealth for different lengths of investment horizons in the UK market. The FTSE All-Share Index is treated as the risky asset, and the UK Treasury bill as the riskless asset in forming the investor's portfolio. Bayesian methods are employed to identify the most powerful predictors by accounting for model uncertainty. It is found that though stock return predictability is weak, it can still affect the investor's optimal portfolio decisions over different investment horizons.

La théorie statistique univariée des valeurs extrêmes se généralise au cas multivarié mais l'absence d'un cadre paramétrique naturel complique l'inférence de la loi jointe des extrêmes. Les marges d'erreur associées aux estimateurs non paramétriques de la structure de dépendance sont difficilement accessibles à partir de la dimension trois. Cependant, quantifier l'incertitude est d'autant plus important pour les applications que le problème de la rareté des données extrêmes est récurrent, en particulier en hydrologie. L'objet de cette thèse est de développer des modèles de dépendance entre extrêmes, dans un cadre bayésien permettant de représenter l'incertitude. Après une introduction à la théorie des valeurs extrêmes et à l'inférence bayésienne (chapitre 1), le chapitre 2 explore les propriétés des modèles obtenus en combinant des modèles paramétriques existants, par mélange bayésien (Bayesian Model Averaging). Un modèle semi-paramétrique de mélange de Dirichlet est étudié au chapitre suivant : une nouvelle paramétrisation est introduite afin de s'affranchir d'une contrainte de moments caractéristique de la structure de dépendance et de faciliter l'échantillonnage de la loi a posteriori. Le chapitre~\ref{censorDiri} est motivé par une application hydrologique: il s'agit d'estimer la structure de dépendance spatiale des crues extrêmes dans la région cévenole des Gardons en utilisant des données historiques enregistrées en quatre points. Les données anciennes augmentent la taille de l'échantillon mais beaucoup de ces données sont censurées. Une méthode d'augmentation de données est introduite, dans le cadre du mélange de Dirichlet, palliant l'absence d'expression explicite de la vraisemblance censurée. Les perspectives sont discutées au chapitre 5.

Esta tesis se ocupa de los problemas de la estimación econométrica con muestras pequeñas, en los contextos del los VARs monetarios y de la investigación empírica del crecimiento. Primero, demuestra cómo mejorar el análisis con VAR estructural en presencia de muestra pequeña. El primer capítulo adapta la especificación con prior intercambiable (exchangeable prior) al contexto del VAR y obtiene nuevos resultados sobre la transmisión monetaria en nuevos miembros de la Unión Europea. El segundo capítulo propone un prior sobre las tasas de crecimiento iniciales de las variables modeladas. Este prior resulta en la corrección del sesgo clásico de la muestra pequeña en series temporales y reconcilia puntos de vista Bayesiano y clásico sobre la estimación de modelos de series temporales. El tercer capítulo estudia el efecto del error de medición de la renta nacional sobre resultados empíricos de crecimiento económico, y demuestra que los procedimientos econométricos robustos a incertidumbre acerca del modelo son muy sensibles al error de medición en los datos. / This thesis deals with the problems of econometric estimation with small samples, in the contexts of monetary VARs and growth empirics. First, it shows how to improve structural VAR analysis on short datasets. The first chapter adapts the exchangeable prior specification to the VAR context, and obtains new findings about monetary transmission in New Member States. The second chapter proposes a prior on initial growth rates of modeled variables, which tackles the Classical small-sample bias in time series, and reconciles Bayesian and Classical points of view on time series estimation. The third chapter studies the effect of measurement error in income data on growth empirics, and shows that econometric procedures which are robust to model uncertainty are very sensitive to measurement error of the plausible size and properties.

This thesis deals with the development, implementation and application of statistical modeling techniques which can be employed in the analysis of credit ratings. Credit ratings are one of the most widely used measures of credit risk and are relevant for a wide array of financial market participants, from investors, as part of their investment decision process, to regulators and legislators as a means of measuring and limiting risk. The majority of credit ratings is produced by the "Big Three" credit rating agencies Standard & Poors', Moody's and Fitch. Especially in the light of the 2007-2009 financial crisis, these rating agencies have been strongly criticized for failing to assess risk accurately and for the lack of transparency in their rating methodology. However, they continue to maintain a powerful role as financial market participants and have a huge impact on the cost of funding. These points of criticism call for the development of modeling techniques that can 1) facilitate an understanding of the factors that drive the rating agencies' evaluations, 2) generate insights into the rating patterns that these agencies exhibit. This dissertation consists of three research articles. The first one focuses on variable selection and assessment of variable importance in accounting-based models of credit risk. The credit risk measure employed in the study is derived from credit ratings assigned by ratings agencies Standard & Poors' and Moody's. To deal with the lack of theoretical foundation specific to this type of models, state-of-the-art statistical methods are employed. Different models are compared based on a predictive criterion and model uncertainty is accounted for in a Bayesian setting. Parsimonious models are identified after applying the proposed techniques. The second paper proposes the class of multivariate ordinal regression models for the modeling of credit ratings. The model class is motivated by the fact that correlated ordinal data arises naturally in the context of credit ratings. From a methodological point of view, we extend existing model specifications in several directions by allowing, among others, for a flexible covariate dependent correlation structure between the continuous variables underlying the ordinal credit ratings. The estimation of the proposed models is performed using composite likelihood methods. Insights into the heterogeneity among the "Big Three" are gained when applying this model class to the multiple credit ratings dataset. A comprehensive simulation study on the performance of the estimators is provided. The third research paper deals with the implementation and application of the model class introduced in the second article. In order to make the class of multivariate ordinal regression models more accessible, the R package mvord and the complementary paper included in this dissertation have been developed. The mvord package is available on the "Comprehensive R Archive Network" (CRAN) for free download and enhances the available ready-to-use statistical software for the analysis of correlated ordinal data. In the creation of the package a strong emphasis has been put on developing a user-friendly and flexible design. The user-friendly design allows end users to estimate in an easy way sophisticated models from the implemented model class. The end users the package appeals to are practitioners and researchers who deal with correlated ordinal data in various areas of application, ranging from credit risk to medicine or psychology.

Biological invasions are reportedly one of the major contributory factors to biodiversity loss worldwide. The impacts of invasive alien plant (IAP) species on native communities are widely documented in the scientific literature, however, there is still a lack of detailed information on their impacts within the most vulnerable habitats. Riparian habitats are highly dynamic systems and naturally disturbed, making them particularly vulnerable to invasion. Climate change, directly or indirectly, is also predicted to adversely impact river systems, which may subsequently alter invasion rates and the impacts of IAPs. However, the interactions between climate and IAPs and their combined effects on vegetation have rarely been examined. To address these knowledge gaps, this thesis investigates: (1) the role of environmental variables, such as sediment loading or climate-related changes to river flow regime, on the abundance of IAPs within riparian zones; (2) how variation in IAP abundance impacts native vegetation, relative to the effects of native dominant plant species and (3) some of the mechanisms underlying the effects of IAPs in riparian habitats. Historic and recent field survey data were used to investigate changes in riparian vegetation on British rivers during the last 20 years. Analyses indicate that IAPs had a negative but small effect on native plant diversity. Overall, changes in land use and differences in flow regime between recording periods were the most important predictors of plant community change. Specifically, IAPs had a greater probability of being present along lowland rivers that experienced increased frequency of high flow events. On a local scale across rivers in Scotland, the abundance of IAPs was constrained by greater soil moisture in summer, whilst greater abundance was associated with tree-lined banks. Both native dominant species and IAPs negatively affected subordinate species abundance to a greater extent than species richness, although this effect varied spatially with bank elevation. Artificial turf mats were used to quantify viable propagules within riverine sediment deposited over-winter along invaded riverbanks. The data indicate that there is a legacy effect of IAP abundance, with the most invaded sites being associated with higher sediment loading the following year, though, contrary to the general pattern, 12 sediment associated propagules were scarcer at invaded sites. Moreover, lower above-ground native diversity was associated with sites which had been previously invaded. Plant species composition in the propagule bank and above-ground vegetation were highly dissimilar, particularly closest to the water’s edge at highly invaded sites. This suggests that mono-specific stands of IAPs proliferate best under less disturbed environmental conditions, although fluvial disturbance events may be required to create opportunities for initial establishment. The propagule bank contributed very little to the above-ground vegetation, nor did it limit invasion, suggesting that above-ground plant composition is largely dictated by competitive interactions. The findings presented in this thesis suggest that invasion by IAPs is an additional stressor for native vegetation within riparian habitats, modifying above-ground plant communities via competition and suppressing recruitment from the propagule bank. However, native dominant species common in riparian habitats also negatively impact, subordinate species via competition, in some cases equalling the effect of IAPs. Native dominant and IAP species are differently affected by environmental factors operating in the riparian zone, which may provide future opportunities for reducing and managing invasions.