4.1 — Panel Data and Fixed Effects - Class Notes


Tuesday, November 10, 2020


Today, we begin our brief look at panel data, where we track multiple individuals over time. Panel data contains its own unique challenges, because it contains a time series component for every individual, giving potential sources of bias.

We now need to understand the third assumption about \(u_i\): no autocorrelation. The errors of our observations are likely going to be correlated within each individual and within each time period.

We can correct for these with a fixed effects model that isolates and absorbs some of that bias. In general, for a two-way fixed effects model:

\[\widehat{\text{Y}}_{it} = \beta_0+\beta_1 \text{X}_{1it} + \beta_2 \text{X}_{2it}+\alpha_{i} + \theta_{t} + \nu_{it}\]

Each observation is an individual \(i\) at time \(t\) (pay attention to the subscripts).


New Packages Mentioned

Note there are several other popular packages (that I have not extensively worked with), such as fixest, lfe.


Homework 5 answers are posted on that page.

Live Class Session on Zoom

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If you are unable to join today’s live session, or if you want to review, you can find the recording stored on Blackboard via Panopto (see Class Recordings on the left navigation menu).