Bayesian Model Averaging (BMA) is a common econometric tool to assess the uncertainty regarding model specification and parameter inference and is widely applied in fields where no strong theoretical guidelines are present. Its major advantage over single-equation models is the combination of evidence from a large number of specifications. The three papers included in this thesis all investigate model structures in the BMA model space. The first contribution evaluates how priors can be chosen to enforce model structures in the presence of interactions terms and multicollinearity. This is linked to a discussion in the Journal of Applied Econometrics regarding the question whether being a Sub-Saharan African country makes a difference for growth modelling. The second essay is concerned with clusters of different models in the model space. We apply Latent Class Analysis to the set of sampled models from BMA and identify different subsets (kinds of) models for two well-known growth data sets. The last paper focuses on the application of "jointness", which tries to find bivariate relationships between regressors in BMA. Accordingly this approach attempts to identify substitutes and complements by linking the econometric discussion on this subject to the field of Machine Learning.(author's abstract)

In the last 20 years spatial econometric models, methods and techniques have been applied to a great variety of empirical problems. The essence of a spatial econometric model is the incorporation of a spatial autoregressive lag, which is scaled by the so called spatial autocorrelation parameter. From a mathematical perspective introducing a spatial autoregressive term into the linear regression model yields a system of equations, which may or may not be solvable for the dependent variable. Furthermore even if the system of equations is solvable, the dependent variable may be diverging if the number of observations approaches infinity. One can show that the solvability and boundedness of the dependent variable in spatial autoregessive models are crucially dependent on the (pre-) specified parameter space of the spatial autocorrelation parameter. Since almost all theoretical work in spatial econometrics assumes both model properties, the validity of spatial econometric methods and techniques is also crucially dependent on the (pre-) specified parameter space. (author's abstract)

This paper introduces a model framework for dynamic credit rating processes. Our framework aggregates ordinal rating information stemming from a variety of rating sources. The dynamic of the consensus rating captures systematic as well as idiosyncratic changes. In addition, our framework allows to validate the different rating sources by analyzing the mean/variance structure of the rating errors. In an empirical study for the iTraxx Europe companies rated by the big three external rating agencies we use Bayesian techniques to estimate the consensus ratings for these companies. The advantages are illustrated by comparing our dynamic rating model to a benchmark model. (author´s abstract) / Series: Research Report Series / Department of Statistics and Mathematics

Posterior analysis in Bayesian model averaging (BMA) applications often includes the assessment of measures of jointness (joint inclusion) across covariates. We link the discussion of jointness measures in the econometric literature to the literature on association rules in data mining exercises. We analyze a group of alternative jointness measures that include those proposed in the BMA literature and several others put forward in the field of data mining. The way these measures address the joint exclusion of covariates appears particularly important in terms of the conclusions that can be drawn from them. Using a dataset of economic growth determinants, we assess how the measurement of jointness in BMA can affect inference about the structure of bivariate inclusion patterns across covariates. (authors' abstract) / Series: Department of Economics Working Paper Series

Wie beeinflusst das gleichzeitige Auftreten von strategischer und umfeldbedingter Unsicherheit das Entscheidungsverhalten? Unterscheiden sich Unternehmer in dieser Hinsicht von Anderen? Die vorliegende Dissertation behandelt diese Fragen und untersucht das Koordinationsverhalten bei dualer Unsicherheit. In vier ökonomischen Experimenten wird das Entscheidungsverhalten von Unternehmern und Nicht-Unternehmern vergleichend analysiert. Die betrachteten Entscheidungssituationen beinhalten Investitionsentscheidungen in Forschung und Entwicklung sowie verschiedene Aspekte des Wettbewerbs und von Markteintrittsentscheidungen. / How do people make decisions when simultaneously facing strategic and environmental uncertainty? Do entrepreneurs differ from others in this regards? This dissertation addresses these questions by investigating coordination behavior under dual uncertainty. Four economic experiments have been conducted comparing the behavior of entrepreneurs and non-entrepreneurs in settings that contain investment decisions into research and development and different aspects of competition and market entry decisions.

Diese Dissertation behandelt verschiedene Aspekte moderner Ökonometrie und Machine Learnings. Kapitel 2 stellt einen neuen Schätzer für die Regressionsparameter in einem Paneldatenmodell mit interaktiven festen Effekten vor. Eine Besonderheit unserer Methode ist die Modellierung der factor loadings durch nichtparametrische Funktionen. Wir zeigen die root-NT-Konvergenz sowie die asymptotische Normalverteilung unseres Schätzers. Kapitel 3 betrachtet die rekursive Schätzung von Quantilen mit Hilfe des stochastic gradient descent (SGD) Algorithmus mit Polyak-Ruppert Mittelwertbildung. Der Algorithmus ist rechnerisch und Speicher-effizient verglichen mit herkömmlichen Schätzmethoden. Unser Fokus ist die Untersuchung des nichtasymptotischen Verhaltens, indem wir eine exponentielle Wahrscheinlichkeitsungleichung zeigen. In Kapitel 4 stellen wir eine neue Methode zur Kalibrierung von conditional Value-at-Risk (CoVaR) basierend auf Quantilregression mittels Neural Networks vor. Wir modellieren systemische Spillovereffekte in einem Netzwerk von systemrelevanten Finanzinstituten. Eine Out-of-Sample Analyse zeigt eine klare Verbesserung im Vergleich zu einer linearen Grundspezifikation. Im Vergleich mit bestehenden Risikomaßen eröffnet unsere Methode eine neue Perspektive auf systemisches Risiko. In Kapitel 5 modellieren wir die gemeinsame Dynamik von Kryptowährungen in einem nicht-stationären Kontext. Um eine Analyse in einem dynamischen Rahmen zu ermöglichen, stellen wir eine neue vector error correction model (VECM) Spezifikation vor, die wir COINtensity VECM nennen. / This thesis focuses on different aspects of the union of modern econometrics and machine learning. Chapter 2 considers a new estimator of the regression parameters in a panel data model with unobservable interactive fixed effects. A distinctive feature of the proposed approach is to model the factor loadings as a nonparametric function. We show that our estimator is root-NT-consistent and asymptotically normal, as well that it reaches the semiparametric efficiency bound under the assumption of i.i.d. errors. Chapter 3 is concerned with the recursive estimation of quantiles using the stochastic gradient descent (SGD) algorithm with Polyak-Ruppert averaging. The algorithm offers a computationally and memory efficient alternative to the usual empirical estimator. Our focus is on studying the nonasymptotic behavior by providing exponentially decreasing tail probability bounds under minimal assumptions. In Chapter 4 we propose a novel approach to calibrate the conditional value-at-risk (CoVaR) of financial institutions based on neural network quantile regression. We model systemic risk spillover effects in a network context across banks by considering the marginal effects of the quantile regression procedure. An out-of-sample analysis shows great performance compared to a linear baseline specification, signifying the importance that nonlinearity plays for modelling systemic risk. A comparison to existing network-based risk measures reveals that our approach offers a new perspective on systemic risk. In Chapter 5 we aim to model the joint dynamics of cryptocurrencies in a nonstationary setting. In particular, we analyze the role of cointegration relationships within a large system of cryptocurrencies in a vector error correction model (VECM) framework. To enable analysis in a dynamic setting, we propose the COINtensity VECM, a nonlinear VECM specification accounting for a varying system-wide cointegration exposure.

Als interdisziplinäre Forschung verbindet diese Arbeit statistisches Lernen mit aktuellen fortschrittlichen Methoden, um mit hochdimensionalität und Nichtstationarität umzugehen. Kapitel 2 stellt Werkzeuge zur Verfügung, um statistische Schlüsse auf die Parameterfunktionen von Generalized Random Forests zu ziehen, die als Lösung der lokalen Momentenbedingung identifiziert wurden. Dies geschieht entweder durch die hochdimensionale Gaußsche Approximationstheorie oder durch Multiplier-Bootstrap. Die theoretischen Aspekte dieser beiden Ansätze werden neben umfangreichen Simulationen und realen Anwendungen im Detail diskutiert. In Kapitel 3 wird der lokal parametrische Ansatz auf zeitvariable Poisson-Prozesse ausgeweitet, um ein Instrument zur Ermittlung von Homogenitätsintervallen innerhalb der Zeitreihen von Zähldaten in einem nichtstationären Umfeld bereitzustellen. Die Methodik beinhaltet rekursive Likelihood-Ratio-Tests und hat ein Maximum in der Teststatistik mit unbekannter Verteilung. Um sie zu approximieren und den kritischen Wert zu finden, verwenden wir den Multiplier-Bootstrap und demonstrieren den Nutzen dieses Algorithmus für deutsche M\&A Daten. Kapitel 4 befasst sich mit der Erstellung einer niedrigdimensionalen Approximation von hochdimensionalen Daten aus dynamischen Systemen. Mithilfe der Resampling-Methoden, der Hauptkomponentenanalyse und Interpolationstechniken konstruieren wir reduzierte dimensionale Ersatzmodelle, die im Vergleich zu den ursprünglichen hochauflösenden Modellen schnellere Ausgaben liefern. In Kapitel 5 versuchen wir, die Verteilungsmerkmale von Kryptowährungen mit den von ihnen zugrunde liegenden Mechanismen zu verknüpfen. Wir verwenden charakteristikbasiertes spektrales Clustering, um Kryptowährungen mit ähnlichem Verhalten in Bezug auf Preis, Blockzeit und Blockgröße zu clustern, und untersuchen diese Cluster, um gemeinsame Mechanismen zwischen verschiedenen Krypto-Clustern zu finden. / As an interdisciplinary research, this thesis couples statistical learning with current advanced methods to deal with high dimensionality and nonstationarity. Chapter 2 provides tools to make statistical inference (uniformly over covariate space) on the parameter functions from Generalized Random Forests identified as the solution of the local moment condition. This is done by either highdimensional Gaussian approximation theorem or via multiplier bootstrap. The theoretical aspects of both of these approaches are discussed in detail alongside extensive simulations and real life applications. In Chapter 3, we extend the local parametric approach to time varying Poisson processes, providing a tool to find intervals of homogeneity within the time series of count data in a nonstationary setting. The methodology involves recursive likelihood ratio tests and has a maxima in test statistic with unknown distribution. To approximate it and find the critical value, we use multiplier bootstrap and demonstrate the utility of this algorithm on German M\&A data. Chapter 4 is concerned with creating low dimensional approximation of high dimensional data from dynamical systems. Using various resampling methods, Principle Component Analysis, and interpolation techniques, we construct reduced dimensional surrogate models that provide faster responses as compared to the original high fidelity models. In Chapter 5, we aim to link the distributional characteristics of cryptocurrencies to their underlying mechanism. We use characteristic based spectral clustering to cluster cryptos with similar behaviour in terms of price, block time, and block size, and scrutinize these clusters to find common mechanisms between various crypto clusters.