Categorical › Logistic regression

Panel data model (fixed / random / pooled)

Panel regression fits pooled OLS, fixed effects (within) or random effects to longitudinal data, with a Hausman test to choose between FE and RE.

What is Panel data model (fixed / random / pooled)?

Panel data follows the same entities over time. Pooled OLS ignores the panel structure. Fixed effects (the within estimator) subtracts each entity's own mean, sweeping out every time-invariant confounder — the workhorse when unobserved entity traits may correlate with the regressors. Random effects treats those entity effects as random draws, gaining efficiency but only valid when they're uncorrelated with the regressors.

The Hausman test formalizes that trade-off: H₀ is that RE is consistent; a small p-value says RE is biased and you should use FE.

When should I use Panel data model (fixed / random / pooled)?

  • Repeated observations on firms, countries, individuals, etc.
  • Controlling for unobserved, time-constant heterogeneity.

What data does it need?

Dependent variable + numeric regressors + an entity index and a time index + model choice.

What does it report?

Coefficients (est / SE / t / p), R², F, and — if requested — the Hausman χ² with an FE/RE recommendation.

What does it assume?

  • Balanced or unbalanced panel with an entity and time key.
  • FE needs within-entity variation in the regressors; a time-invariant regressor drops out.
  • Standard panel-model inference assumes homoskedastic, serially uncorrelated errors (cluster-robust SEs are a natural extension).

How do I interpret the result?

FE coefficients are within-entity effects. If the Hausman test rejects, prefer FE; otherwise RE is more efficient.

See also

References

  • Croissant & Millo (2008). Panel data econometrics. JSS 27.
  • Wooldridge (2010). Econometric Analysis of Cross Section and Panel Data.