Quantitative Research Methods
Course code
QUANT301, QUANT303
Course type
BSc Course
Weekly Hours
4,0
ECTS
6.0
Term
FS 2020
Language
Englisch
Lecturers
Prof. Dr. Michael Massmann
Please note that exchange students obtain a higher number of credits in the BSc-program at WHU than listed here. For further information please contact directly the International Relations Office.
Course content
The purpose of this course is to introduce modern econometric techniques and apply them to real world data sets. The material covered in this course includes an in-depth analysis of the linear regression model, least-squares estimation and statistical inference in this setting. Subsequently, nonlinear regression models are discussed and an introduction to panel data as well as stationary time series analysis is given. Empirical data sets are taken from finance and macroeconomics.
Class dates
Date | Time |
---|---|
Monday, 06.01.2020 | 11:30 - 15:15 |
Wednesday, 22.01.2020 | 11:30 - 15:15 |
Wednesday, 04.03.2020 | 11:30 - 15:15 |
Friday, 13.03.2020 | 11:30 - 12:30 |
Friday, 20.03.2020 | 13:00 - 15:15 |
Monday, 30.03.2020 | 11:30 - 15:15 |
Thursday, 09.04.2020 | 15:30 - 17:00 |
Wednesday, 29.04.2020 | 14:00 - 17:00 |
Learning outcomes
By the end of this course, students will have a sound understanding of fundamental
econometric techniques and will be able to apply them to real world data sets using
modern software.
econometric techniques and will be able to apply them to real world data sets using
modern software.
Literature
Stock and Watson (2015): Introductory Econometrics
Learningmethods
The course takes the form of interactive lectures with exercises: on the one hand, theoretical material is presented and illustrated by means of empirical examples using the R statistical computing environment; on the other hand, participants are given exercises to practice the use of newly-learned concepts, both conceptually and on the computer.
Exam
written exam
Requirements
Familiarity with the topics covered in Statistics I and II is assumed.
Total workload
180