KINT 3000 - Applied Econometrics with Stata - Beginners

The course provides an introduction to statistical analysis using Stata. The course will introduce students to data cleaning, providing meaningful descriptive statistics, running ordinary least squares regressions and interpreting results. A particular focus is placed on best practices in coding and using precise language in statistics. If time permits, the students will learn about methods for causal inference.

Learning Outcomes

• Interpret OLS regressions and tables
• Discuss statistical and economic significance
• Develop skills and intuition for pointing out statistical errors or misrepresentations

Professionnal Skills

Set up reproducible research projects in Stata and Github; Present results to various audiences.

The goal of this course is to make students able to apply an econometrics reasoning to study economics issues. To do this, the course is broken down into several periods throughout the semester with 1) Reminder of mathematics and statistics, 2) OLS estimate and topics related, and 3) Introduction to causal estimates. Regarding the level of the students at the beginning of the semester, the objective is to make them able to attend the advanced econometrics course.

The teaching is divided into three categories with 1) The in-class course which will present theories, intuitions, and Stata commands, 2) The online sessions which will be fully dedicated to Stata coding and 3) Meetings during the semester to discuss the research project.

Before coming to class, students must have worked on the course, read the required chapters, and completed the exercises on Stata. Each week students will have to prepare and submit a Problem Set, generally a Stata do-file, based on the previous week's tutorial. Learning to code requires a lot of trial and error. Students are expected to be autonomous and seek out answers on their own using resources discussed in class.

None

• Two take home assignments 50%
• Project and presentation – Assigned around Week 4, Due around Week 10 50%

• In Class Presence: 2 hours a week / 24 hours a semester
• Online learning activities: 10 hours a semester
• Reading and Preparation for Class: 4 hours a week / 48 hours a semester
• Research and Preparation for Group Work: 26 hours a semester
• Other: Problem Sets / Coding: 4 hours a week / 48 hours a semester