Study location	Netherlands, Maastricht
Type	Online Summer Courses, Full-time
Nominal duration	1 week (2 ECTS)
Study language	English

Tuition fee	€649 one-time

Entry qualification

Enrolled as an Undergraduate student or Undergraduate diploma - Statistics: Descriptive Statistics, Inferential Statistics (Multivariate Regression, ANOVA). Preferably had an introduction about ARMA models, but not mandatory. - Fundamental knowledge in finance (type of financial instruments, understanding of markets, familiar with financial jargon) - Beginner knowledge in R and RStudio The entry qualification documents are accepted in the following languages: English. Often you can get a suitable transcript from your school. If this is not the case, you will need official translations along with verified copies of the original.

Language requirements	English The language of the course is English, so we expect a fluent level and the ability to follow and participate in class.

More information	maastrichtsummerschool.nl

Overview

Course Description
This course delves into some of the statistical methods used to estimate causality in financial contexts. The course will cover, among other methods, Difference-in-Differences, and Interrupted Time Series.
During the course, each day will be dedicated to a specific method. We will start the day by studying a specific causality problem. We will then apply the method to our problem intuitively. In a second time, we will go over the mathematics of the method more formally and dive into further details. There will be short assignments to do every day about the method covered.
The course uses R programming to perform statistical analysis.
This course is addressed to third-year bachelor students who have an interest in developing their statistical toolkit in finance and first-year master students who want to consolidate their knowledge of causality in finance.

Goals
• Understand the advantages and drawbacks of each method presented in the course.
• Apply the right statistical method to a financial causality problem.
• Use R programming to perform all methods covered in class appropriately.
• Reflect critically when interpreting the results given by the chosen statistical method.

Teaching Methods
Lectures, Workshops

Assessment Methods
Assignment, Attendance

Course Coordinator
Mathis Mourey