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)
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