Advanced Regression Methods in Finance and Economics: Three Essays

In this thesis advanced regression methods are applied to discuss and investigate highly relevant research questions in the areas of finance and economics. In the field of credit risk the thesis investigates a hierarchical model which allows to obtain a consensus score, if several ratings are available for each firm. Autoregressive processes and random effects are used to model both a correlation structure between and within the obligors in the sample. The model also allows to validate the raters themselves. The problem of model uncertainty and multicollinearity between the explanatory variables is addressed in the other two applications. Penalized regressions, like bridge regressions, are used to handle multicollinearity while model averaging techniques allow to account for model uncertainty. The second part of the thesis makes use of Bayesian elastic nets and Bayesian Model Averaging (BMA) techniques to discuss long-term economic growth. It identifies variables which are significantly related to long-term growth. Additionally, it illustrates the superiority of this approach in terms of predictive accuracy. Finally, the third part combines ridge regressions with BMA to identify macroeconomic variables which are significantly related to aggregated firm failure rates. The estimated results deliver important insights for e.g., stress-test scenarios.