Week 11/12 - Panel data

Content for week of Monday, April 4, 2022–Friday, April 15, 2022

Overview

We’ve complained a lot about challenges to obtaining causal estimation. Now, let’s do something about it! :strong:

Note that this week we’re covering difference-in-differences estimation - it’s not actually in Chapter 101, but it fits here nicely.

Reading Guide

Chapter 10: Regression with Panel Data

SW 10.1 Panel data

Make sure you know the difference between cross-sectional, time-series, and panel data. Also, what does it mean to be balanced?

SW 10.2 Panel data with two time periods: “Before and after” comparisons

We can control for variables that are constant over time by differencing them out. Do you understand how this works?

SW 10.3 Fixed effects regression

Instead of differencing, let’s include a bunch of entity-specific intercepts. What are we doing, why does it work? When is it like the before-after comparisons from before?

SW 10.4 Regression with time fixed effects

Just like we can control for unit-specific factors that remain constant over time, we can also control for factors that vary over time but are constant across units with time fixed effects. Neat!

Can we include both entity- and time-specific fixed effetcst? You bet we can!

SW 10.5 The fixed effects regression assumptions and standard errors for fixed effects regressions

How do our LS assumptions change when we move to panel data?

SW ?? Difference-in-differences estimation

Set up and interpret difference-in-differences estimation. There’s a decent discussion in SW13.4, but I think it’s more appropriate to cover it here, since it’s so closely linked to panel data.

  1. Check out SW13.4 if you want to be precise.↩︎

Last updated on March 21, 2022

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