This course, jointly with the Introductory Applied Econometrics course, provides the most comprehensive and serious overview of first-year Econometrics available, to date, on Udemy.

I don't have to be here. But if I am going to be here, I am going to do it right and set the benchmark as to how Economics should be taught. Because I take students and their exams and their personal development seriously (on that note, feel absolutely free to reach out for any question or doubt that may arise as you delve into the material). Because, when I was taken seriously by my professors, everything became clearer and more engaging. Because the world is in dire need of engaged, curious people who act according to the brains instead of their stomach, people who let serious social science guide their gaze upon the surrounding world instead of random nonsense. A thoughtful world is a better world. I am strongly convinced that a serious study of proper economics helps moving toward that end.

In this course, I set out to introduce the first-year sequence by introducing the main methods in Econometrics. Its purpose, shared by the Introductory Applied Econometrics course too, is to lay the foundations for deeper and more comprehensive studies in Econometrics. Hence, I spend little time dwelling on the mathematical derivation and statistical nuances, covering only the bare minimum, and more time trying to convey the intuition, the concept and, essentially, why should you care. Namely, after having introduced the Simple Linear Regression Model, I set out to illustrate the Multiple Linear Regression Model, Functional Forms, Dummy Variables (both as Independent and Dependent Variables) and the main issues that plague Linear Regression Models (namely, Heteroskedasticity, Multicollinearity and Endogeneity) and their solutions. I cap it off by providing you with a list of Applications, real world examples of what kind of questions these models allow you to address, in the hope that they fire your curiosity up. Which is the only thing that matters.