Welcome to Applied Econometrics 1!

Someone once said that, "There is nothing more dangerous than an undergrad armed with OLS." Unfortunately, this is also true for graduate students up to experienced researchers. In response to this, I revisit both the key conceptual and mathematical ideas behind probability theory, statistical theory, and econometric theory. In this course, we try to answer some research questions related to classic questions in economics with the help of economic theory, the data, and econometric methods.

Information about using the slides

Lecture Materials for Applied Econometrics 1 (2022 version) by Andrew Adrian Pua is licensed under Attribution-ShareAlike 4.0 International

To cite these slides, please use
Pua, Andrew Adrian. 2022. "Lecture Materials for Applied Econometrics 1 (2022 Version)." https://applied-metrics.neocities.org/.

Scope of final exam

Individual paper details

Book readings

Practice exercises here

Week 10:

For this week, I will talk about how linear regression, which is about making predictive comparisons, may provide causal effects and how instrumental variables could be a strategy in case linear regression does not work.

Week 9:

For this week, I will talk about expressing uncertainty and conducting inference in linear regression settings. I also introduce the bootstrap principle (if only we have time: but we didn't).

Weeks 5 to 8:

For these weeks, I will talk about what we could learn from running regressions, while introducing basic concepts of probability and statistical inference.

Weeks 1 to 4:

For these weeks, I talk about what the course is all about and move on to the concept of a distribution and the different ways of describing them. This also serves as a crash course in linear regressions from descriptive point of view.

Highlighted resources and links:

Study and time management resources