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Course Details



Prof. Okhrin
WIW-5220


Econometrics

Lecturer: Prof. Dr. Yarema Okhrin
Type: Lecture
Exam: Written exam
ECTS: 6
Term: Winter
Level: Master
Limitation: None

Professional competencies: After successfully participating in this module, the students understand econometric modeling methods, in particular those of multiple linear regression. They understand the assumptions of regression modeling and are familiar with alternative approaches that take violated model assumptions into account. Methodological competencies: The students are able to carry out an extensive regression analysis. They use statistical tests to assess the significance of the regression parameters. They are also capable of detecting heteroscedasticity and autocorrelation and know methods which help to overcome these problems. The students know alternative estimation methods, such as ML, IV, GMM, and their advantages. They are able to compare alternative models using rigorous statistical techniques such as Wald and LR tests. The students are able to recognize structural breaks in the data and they also know extensions of the classical regression model (non-linear regression, regression with chronological data, etc.).The participants of the course can interpret and critically evaluate the results of regression modeling. In addition, the students are able to apply the statistical methods presented in the course using the statistical programming language R and carry out and correctly interpret an empirical analysis using R on their own. Interdisciplinary Skills: The students are able to apply the acquired knowledge in all areas of study, which deal with empirical data. They understand which methods should be appropriately applied for different empirical data sets and know how to interpret the results, depending on the respective economic question at hand. Key competencies: The students of the course are able to select a suitable econometric model for a given data set, and are able to empirically estimate a correct model using appropriate methods. They are able to interpret the results of the estimation both from a mathematical and also an economic point of view. Additionally, they are able to find causal relationships in economic data and are able to employ methods to judge the quality of econometric models.


For further information or application procedure please check the module manual and the website of the chair.