Fixed vs. Random Effects

The best way to think about the difference between random and fixed effects is with this picture.

Fixed effects can be thought of as the relationship between predictor and outcome within an entity. In addition:

  • Assumes something about entity may bias predictor/outcome so need to control for it
  • Removes effects of observed or unobserved time-invariant characteristics from predictor variables
  • It helps with omitted variables bias
  • Creates separate regressions for each entity and averages effects across entities

Random effects, on the other hand, vary across entities

  • Assumes random and uncorrelated with IVs
  • Can include time-invariant variables
  • Assumes entity’s error term is not correlated with predictors which allows time-invariant variables can be explanatory variables

Some examples include:

  • Time-varying observables – age, years of experience
  • Time-invariant observables – degree, gender
  • Time-invariant unobservables – ability, IQ
  • Omitted variables are time invariant
(Adapted from course notes)
(Flashcards and other resources here)
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