Week 11/12 - Panel data
Overview
We’ve complained a lot about challenges to obtaining causal estimation. Now, let’s do something about it! :strong:
Tuesday covers panel data, first differencing, and fixed effects — the tools that let us eliminate time-invariant omitted variable bias.
Thursday covers difference-in-differences (DiD) — a research design that uses these tools to estimate the causal effects of policies and events. DiD isn’t 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.
Slides
In-Class Activity
From Tuesday 4/7, here are the fixed effects handout and solution set:
- Fixed Effects Showdown activity (PDF)
- Fixed Effects Showdown answer key (PDF) (try the activity first if you want the full practice effect)
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Check out SW13.4 if you want to be precise. ↩︎