Quantitative Research Methods

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.

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.

The entire course relies on the following text. Stock, J. H. & M. W. Watson (2015). Introduction to Econometrics. 3rd updated ed. Boston: Pearson. ISBN: 9781292071312. URL: http://wps.pearsoned.co.uk/ema_ge_stock_ieupdate_3

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.

At the time of writing, the plan is to have a traditional in-person written exam at the end of this course. Those who cannot physically attend or are not allowed to be present in the exam hall will be offered an online exam. If necessary, the exam will be online for everyone. As in previous years, the exam will solely consist of open questions. Each question will contain several sub-questions, ranging in difficulty from straightforward to advanced. Note that the exam will be open-book and open-notes. The WHU calculators of the Casio FX82 series only are allowed during the exam.To account for the uncertainty surrounding the exam situation, the exam mark will only count 80% towards the final grade of this course. The exam will be 2½ hours long.

The remaining 20% of the final grade will be based on two assignments. This will allow participants to earn credit without the pressure of technological complications and immediate time constraints. Each assignment, worth 10% of the final grade, will require an empirical analysis in R. A 3-page report, excluding graphs and tables, will have to be written in RMarkdown. The assignments may be completed in teams of two students. These teams must be different for the two assignments, however. Reports will checked for plagiarism. If plagiarism is detected, all teams concerned will be disqualified.

Familiarity with the topics covered in Statistics I and II is assumed.

180 hrs