This course introduces the foundational concepts of econometrics. Most importantly, we will cover the linear conditional expectation function and linear regression. Toward the end of the course, we will touch on the potential outcomes framework and provide a brief introduction to causal inference. The primary goal of this course is for you to gain an in-depth understanding of linear regression. While we emphasize basic concepts, econometrics is fundamentally a practical tool. A significant portion of the assignments will focus on practical applications. We adopt a hands-on approach, guiding you to explore and utilize relevant resources effectively. Think of this course as a workshop designed to build your data analysis skills within the field of econometrics. Mastering these concepts requires considerable effort. As a guideline, plan to dedicate approximately three hours of study per week for each credit hour. This course carries 4 credits, so you should expect about 20 hours of weekly engagement. This includes two hours of in-class interaction, leaving 18 hours for independent study and assignments. Each week, you will have access to 1–2 hours of pre-recorded lectures, complemented by 4 hours of practical exercises on DataCamp. Additionally, plan for approximately 4 hours of homework and supplementary reading each week. Full Syllabus: https://drive.google.com/file/d/1Fhi0Suh1l_q5I50hwiFXZGmLfbuVzNir/view?usp=sharing

The primary goal of this course is for you to gain an in-depth understanding of linear regression. You will know what is linear regression, when and how to use it, and how to interpret the results.

Angrist, J. (2014): Mastering’metrics: The path from cause to effect, Princeton University Press. Angrist, J. D. and J.-S. Pischke (2009): Mostly harmless econometrics: An empiricist’s companion, Princeton university press. Hansen, B. (2022): Econometrics, Princeton University Press.