Applied Statistics and Econometrics

Introduction: Random variables, measuring scales and sampling procedures. The linear model. Properties of regression coefficients and hypothesis testing. Multiple regression analysis. Dummy variables. Heteroscadisticitiy. Time Series Analysis. Panel Data. Stochastic regressors and measurement errors. Binary choice and limited dependent variables. Simultaneous equation estimation.

After successfully completing the module, students will have a thorough understanding of the application of the basic econometrics methods outlined above in the section "Inhalt" [Course Contents] that have become indispensable for a proper understanding of the current literature in the spheres of economics, business and management. With the econometric tools learnt, students will be capable of independently analysing economic data of cross-section, time series and panel structure, The students are able to describe the data, select the appropriate models, perform tests on the data structure and know the economic interpretation of the estimation results.

Prüfungsdauer (in min.): 120. The course covers the understanding of data generating processes of economic data and the selection and application of econometric models for their analysis. The students acquire methodological knowledge and analytical competences. After completing the course the students can reproduce and apply the methods learned in the lecture and lab exercises. A written examination is deemed the most appropriate mode of examination. The written examination includes a theoretical part with short questions (20 min.) and a second part with estimation of econometric models and analysis and interpretation of the results. Students are allowed to use the econometric software on a computer, a short formula collection (2 DinA4 handwritten pages) can be brought to the written exam. The examination result contributes 100% towards to the final course mark. The examination carries a pass mark of at least 50%.

An undergraduate course in applied statistics covering random variables, sampling, hypothesis testing, simple regression analysis, multiple regression analysis, assumptions for linear regression models with nonstochastic explanatory variables.

Lectures will be used to teach econometric assumptions and models. Exercises will be used to apply the models taught in the lectures to provided data sets and applied economic analysis questions.