Fixed Effects Model
Updated 2026-08-15
INTRODUCTION
English translation pending.
CORE DEFINITION
The fixed effects model treats each individual's distinctive traits as fixed, estimable parameters rather than as noise, and identifies the effect from within-unit variation over time. By sweeping out everything constant about an individual, it controls for unobserved heterogeneity that never changes, separating what a variable actually does from what the individual already was before it changed.
SCAFFOLDING EFFECT
Reduce cognitive load
- Control individual differences: strip the unit's constant traits out of the estimate - Read within-unit change only: interpret a coefficient as what happens when this same unit changes - Know the blind spot: the effects of time-invariant variables cannot be estimated at all
Anchor fast decisions
Each individual differs in ways that are never fully measured, and these constants would contaminate any cross-sectional comparison. Removing each unit's own mean deletes every characteristic that never changes for it, whether observed or not, so the variation that remains is change within the same unit. The coefficient then reflects covariation free of standing differences.
MINIMUM ACTION
In progress 0/1Practice this model in one real situation:
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Source support: Explicit
- en.wikipedia.orghttps://en.wikipedia.org/wiki/Fixed_effects_modelverified
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