## When to use fixed effects vs random effects models?

### Fixed effects and random effects models are statistical techniques used in analyzing panel or longitudinal data. The choice between these models depends on the specific research question and assumptions about the data.

Fixed effects models are appropriate when there is a need to control for unobserved time-invariant heterogeneity among the individuals or entities in the data. In other words, fixed effects models are used when we want to account for individual-specific characteristics that do not change over time but may still influence the dependent variable. By including individual-specific fixed effects, we can effectively eliminate the influence of these time-invariant characteristics and focus on the time-varying aspects of the data. Fixed effects models are useful when studying the effects of a treatment or intervention on individuals over time, as they help to capture within-individual changes.

On the other hand, random effects models are suitable when the focus is on modeling the variability of the individual-specific effects. Random effects models assume that the individual-specific effects are random draws from a larger population of effects. These models are particularly useful when we are interested in understanding the overall distribution of effects across the population of individuals under study. Random effects models generally require fewer assumptions compared to fixed effects models but may yield less precise estimates of individual effects.

Therefore, the choice between fixed effects and random effects models depends on the underlying research objectives and assumptions about the data. Both models have their strengths and limitations, and researchers often evaluate the robustness and sensitivity of their findings to the choice of model.

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