Quantitative Research Methods

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.

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