CC 502: Applied Econometrics
Contents
In this module we first briefly review most essential statistical concepts from descriptive and inferential statistics for univariate and bivariate data. Upon this, some concepts are extended or generalized to higher-dimensional data settings. The second part will mainly provide a treatment of the principles and uses of (linear) regression analysis for various purposes, such as causality analysis, prediction and forecasting. We will learn how the results from such analyses are appropriately interpreted and will discuss the limitations and potential pitfalls of all these techniques as well.
The Statistical Software R will intensively be used throughout the course and also in the final exam (laptop required).
Learning outcomes
By the end of the module students will have
- a sound understanding of key statistical concepts and techniques,
- familiarity with the principles and core techniques of econometric analysis and how regression results are used and interpreted,
- trained skills in the practical application of these techniques in a programming language
Necessary prerequisites
not taken module CC 503
Recommended prerequisites
knowledge of basic statistics (elementary probability theory and inferential statistics included) at bachelor level required, knowledge of elementary linear algebra (vectors and matrices) helpful, should also know the concept of random variables and expected values
| Forms of teaching and learning | Contact hours | Independent study time |
|---|---|---|
| Lecture | 2 SWS | 6 SWS |
| Exercise class | 2 SWS | 7 SWS |
| ECTS credits | 6 |
| Graded | yes |
| Workload | 180h |
| Language | English |
| Form of assessment | Written exam (90 min) |
| Restricted admission | no |
| Further information | – |
Examiner Performing lecturer |  | Dr. Toni Stocker Dr. Toni Stocker |
| Frequency of offering | Fall semester |
| Duration of module | 1 semester |
| Range of application | M.Sc. MMM, M.Sc. MMFACT |
| Preliminary course work | – |
| Program-specific Competency Goals | CG 1, CG 2 |
| Literature |
|
| Course outline | 1 Intro and Overview 2 Distributions, Dependences and Quantities 3 Basics Extended (p>2) 4 R: Introduction to R 5 R: Visualizing and Exploring (R) 6 Simple Linear Regression 7 Simple Linear Regression Model (SLRM) 8 Partial and Multiple Linear Regression 9 Multiple Linear Regression Model (MLRM) 10 Regression with Qualitative Dependent Variables 11 Time Series Regression and Forecasting Remark: The statistical Software R will intensively be used throughout the course (laptop required) and also in the final exam |