Introduction to ECON 8080

Brantly Callaway

University of Georgia

Main Topics for this Semester

\(\newcommand{\E}{\mathbb{E}} \newcommand{\E}{\mathbb{E}} \newcommand{\var}{\mathrm{var}} \newcommand{\cov}{\mathrm{cov}} \newcommand{\Var}{\mathrm{var}} \newcommand{\Cov}{\mathrm{cov}} \newcommand{\Corr}{\mathrm{corr}} \newcommand{\corr}{\mathrm{corr}} \newcommand{\L}{\mathrm{L}} \renewcommand{\P}{\mathrm{P}} \newcommand{\T}{\mathrm{T}} \newcommand{\independent}{{\perp\!\!\!\perp}} \newcommand{\indicator}[1]{ \mathbf{1}\{#1\} }\)The main two topics that we’ll cover this semester are:

Linear Regression
Panel Data
plus a few others along the way

Throughout the semester, we’ll also mostly be interested on learning tools/concepts that are useful for conducting empirical research in economics

In practice, a whole lot of research questions are like: how did some policy/intervention/etc. cause some outcomes of interest to change relative to what they would have been in the absence of the policy/intervention?

We will have these sorts of questions in mind (or at least in the back of our minds) throughout the semester

Introduction

The course is also relatively theoretical:

One version of this class might primarily teach you which buttons to click in some statistical programming language and under which situations

In this class, we’ll go into substantial detail on topics and try to figure out how things work
- For example, for all the estimators that we talk about this semester, we will write the code to implement them ourselves rather than relying on “canned” implementations from R.
- We’ll also spend a lot of time on theory related to conducting inference and understanding the statistical properties of different estimators — this will require some challenging math

Statistical Programming

This semester, we’ll do a lot of statistical programming, using the R language

Please see the syllabus for additional resources on R programming

Our TA, Derek, is a very good R programmer, and he is a very good resource this semester

I expect the difficulty level on this front to be relatively high

Other Resources

On the syllabus, there are a number of additional resources. These are mostly at the Ph.D. or M.A. level. If you need more introductory material on any topics here are some suggestions:

I have very detailed course notes for my undergraduate class (both on R and on Econometrics) https://bcallaway11.github.io/Courses/ECON_4750_Fall_2024

A relatively short introduction to R from Stephanie Spielman: https://sjspielman.github.io/datascience_for_biologists/tutorials/introduction_to_R.html

I also like Stock and Watson and Wooldridge as undergraduate level books.
- They both cover a lot of the same material that we will cover in this course, though at an easier level.
- [If you get either of these, get an older edition and save some money.]

Linear Regression

ECON 8070 ended with an introduction to linear regression. Linear regression will be our first main topic for this semester.

There may be some overlap with our class, but that is by design.

That said, we have been totally reworking our Ph.D. sequence in Econometrics last year and this year. I’m open to feedback during the semester about the difficulty level, pace, and (to some extent) topics.

We will talk in great detail about:

How to interpret regressions in different settings or under different assumptions
Theoretical properties of regression estimators
Some pros and cons of using linear regression to think about causal effects

Panel Data

The second main topic for the semester is panel data

Panel data is data where we we can follow the same unit (this could be an indivdual, firm, etc.) over time

Panel data is especially useful for thinking about causality
- This my main research area and one that I think is very interesting and useful.
- Intuition: If you want to understand the causal effect of some policy/intervention, it is very useful to have information about a person’s outcomes before they were affected by the intervention as well as to have information about how outcomes evolved for other (hopefully “similar”) people who did not participate

Brief Outline of the First Few Weeks

I will spend a handful of classes doing an introduction to R programming

Then, we will have a discussion of some classical motivations for running regressions

Next, we will consider why you might want to use a linear regression to think about causal effects (along with some possible weaknesses)

Then, we will spend several weeks learning about how regressions work, how to conduct inference, etc. We will cover this in much detail.
- We will also cover the “theory” of linear regressions extensively, both for its own sake and because a lot of those arguments will be applicable to other (and more complicated) estimators.

Last comment before we get going…

My super-high-level advice about being a 1st year graduate student and becoming a researcher:

In economics, we lead with theory in the first year.

It may be easy to get lost in the math/programming this semester
Being a successful economist/researcher does require technical skills, but (in my opinion) it is more about being able to think systematically, forming good questions, and being creative

It’s an exciting time to be working with data, but…

The Achilles heel of empirical research is the tendency to “find results” that aren’t real
Once you start your own research, don’t be a salesman, be a scientist!