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Wind is used as an energy source all over the world. To be able to use this effectively, there is a need for as good forecasts and forecast models as possible. One of these models is Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS®) that is used to calculate short time forecasts. This model is used here to calculate wind speeds at two different areas in Västra Götaland, Bengtsfors and Vänersborg. There are also wind measurements with SODAR stations for these areas. The first part of this work is to investigate the difference between two model resolutions, 36 and 12 km, for the model results compared with the measurements. The comparison is done by calculating some different statistical values. The results of these parameters show that the difference between the two resolutions is fairly small and that the lower resolution gives a slightly better result. The second and major part of this work is to use two different regression models to adjust the result of the forecast models to the result of the measurements. These regression models will then be possible to use even when there are no measurements to compare with. The idea of these regression models is to find a way to describe the difference between the result of the forecast model and the SODAR measurements. This difference is then subtracted from the result of the forecast model so that you get an adjustment and more accurate result. The first regression model calculates the difference according to time of the day, the other model calculates the difference according to the wind speed. Furthermore, the measurements used are taken from 75 meters height above the ground. These are then compared to some different results from the forecast model, for example different model heights and different resolutions, and also the model results adjusted with the regression models. The comparison is done by calculating the same statistic values as before, both with and without an adjustment with the regression models, and also to look at histograms that show the distribution of the difference. It is shown that with the regression adjustment, there is a clear improvement of the statistical values compared to the original results of the forecasts. For example the value of the absolute mean difference is reduced with approximately 0.4-0.7 m/s with an adjustment of the regression model. The histograms clearly show that a more even distribution occurs after the adjustment with the regression models. From having a major part of the differences at 1-2 m/s to now having the major part at around 0 m/s and furthermore there is also generally a lower difference between the measurements and the results from the forecast model.

Die Kapitel 1 bis 6 im ersten Teil dieses Skriptes beruhen auf einer Vorlesung Ökonometrie I, die zuletzt im WS 2001/02 gehalten wurde, die Kapitel 7 bis 16 beruhen auf einer Vorlesung Ökonometrie II, die zuletzt im SS 2006 gehalten wurde. Das achte Kapitel enthält eine komprimierte Zusammenfassung der Ergebnisse aus dem Teil Ökonometrie I.

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. (author's abstract)