CC 502: Applied Econometrics

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.

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,
  • skills in the practical application of these techniques.

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 learningContact hoursIndependent study time
Lecture2 SWS6 SWS
Exercise class2 SWS7 SWS
ECTS credits6
Graded yes
Form of assessmentWritten exam (90 min.)
Restricted admissionno
Further information
Performing lecturer
Dr. Toni Stocker
Dr. Toni Stocker
Frequency of offeringFall semester
Duration of module 1 semester
Range of applicationM.Sc. MMM, M.Sc. Bus. Edu.
Preliminary course work
Program-specific Competency GoalsCG 1, CG 2
  • J. H. Stock, M. W. Watson (2020): Introduction to Econometrics; Pearson Global Edition 4th ed.
  • J. M. Wooldridge (2020): Introductory Econometrics – A Modern Approach; Cengage 7th ed.
  • C. Heij. et. al. (2004): Eonometric Methods with Applications in Business and Economics; Oxford UP.
  • T. Stocker, Steinke I. (2022): Statistik – Grundlagen und Methodik; De Gruyter Oldenbourg 2nd ed. (only in German)
Course outline1 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