Online Course Guide of WHU –

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Please use the filters below to select the term (spring or fall) as well as the respective program (BSc, MSc, MBA, Exchange, Doctoral) of your choice for an overview of all modules offered at WHU. The courses are listed under the modules. Please click on a module to see which courses are part of it. If you would like to find out more about a certain course, click on the name of the course to see detail information. The location of the lecture will be revealed after your course registration on myWHUstudies.

Spring term counts from January - August, fall term counts from September - December.

Important for Exchange Students: As the Full-Time and Part-Time MBA Programs utilize a modular course structure, the dates on which students begin and end the exchange are flexible. Please find here a chronological overview of the preliminary course offering for Fall and Spring.

Spring 2022  ›  Bachelor of Science  ›  Bachelor of Science - 4th Semester  ›  Quantitative Research Methods

Quantitative Research Methods

Course Code:
QUANT301, QUANT303
Lecturers:
Prof. Dr. Michael Massmann
Course Type:
BSc Course
Week Hours:
4,0
Term:
Spring 2022
Language:
Englisch
Credits:
6.0
(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.)
The purpose of this course is to introduce state-of-the art econometric techniques and apply them to real world data sets. The methods covered in this course include an in-depth analysis of the workhorse in data science, viz. the linear regression model and least-squares estimation, as well as of statistical and causal inference in this setting. Subsequently, advanced techniques such as nonlinear regression models are discussed and an introduction to panel data and stationary time series analysis is given so as to cater for complex data structures frequently encountered in applied work. The empirical analyses are implemented in RStudio, the most popular data science software environment in academia and finance, and in RMarkdown, the prime language for producing replicable research.
Date
Time
Monday, 10/01/2022
11:30 AM till 03:15 PM
Friday, 14/01/2022
11:30 AM till 03:15 PM
Monday, 17/01/2022
11:30 AM till 03:15 PM
Monday, 24/01/2022
11:30 AM till 03:15 PM
Monday, 31/01/2022
11:30 AM till 03:15 PM
Monday, 07/02/2022
11:30 AM till 03:15 PM
Monday, 14/02/2022
11:30 AM till 03:15 PM
Monday, 21/02/2022
11:30 AM till 03:15 PM
Monday, 14/03/2022
11:30 AM till 03:15 PM
Monday, 21/03/2022
11:30 AM till 03:15 PM
Monday, 28/03/2022
11:30 AM till 03:15 PM
Monday, 04/04/2022
11:30 AM till 03:15 PM
By the end of this course, students will be familiar with modern econometric methods and will be able to apply them to real world data sets using state-of-the-art software. Students will have acquired a sound theoretical mindset for data analysis as well as causal inference. They will have developed programming skills for conducting replicable empirical work. This proficiency will prove indispensable for their Bachelor's thesis, for a Master's degree or for data science projects in industry.
Stock and Watson (2015): Introductory Econometrics. This is the best textbook around, ideally suited for students in business and economics, and used all around the world. The authors are world-class econometricians based at Harvard and Princeton, respectively.
The course takes the form of interactive lectures with exercises: on the one hand, theoretical material is discussed in class and illustrated by means of empirical examples in live demonstrations in RStudio; on the other hand, participants are given theoretical and coding exercises to practice the use of newly-learned concepts.
Written examination (80%) and assignments (20%).
Familiarity with the topics covered in Statistics I and II is assumed.
180 hrs