Regression modelEconometricsModel

Fixed Effects Panel Model

Q: How do I choose between fixed and random effects?

Run a Hausman test. If it rejects the null (p < 0.05), the unit effects are correlated with the regressors and the random effects estimator is inconsistent, so fixed effects should be preferred. If it does not reject, random effects is the more efficient choice.

Q: Why can't fixed effects estimate time-invariant variables?

The within transformation subtracts each unit's own time-average, which removes anything constant for that unit — including the fixed effect and any time-invariant predictor. Their coefficients are therefore not identified. If such variables are central, consider the random effects model instead.

Q: What standard errors should I report?

Errors are usually correlated within a unit across time, so cluster-robust standard errors clustered by entity are recommended; default standard errors would understate the true uncertainty.

Q: What if endogeneity remains after fixed effects?

Fixed effects only removes time-invariant unobserved heterogeneity. If a regressor is still correlated with the idiosyncratic error, the estimates are biased and an instrumental-variable or system-GMM approach is required.

Also known as: within estimator, panel fixed effects, entity fixed effects model, Panel Sabit Etkiler Modeli

OriginatorBaltagi (textbook treatment); Hausman test for FE vs RE choiceYear2005Sources2Related methods5

The fixed effects panel model estimates relationships in panel data (many units observed over time) by exploiting only the within-unit variation, so that unobserved time-invariant heterogeneity is controlled away. It is the central within estimator developed in Baltagi's Econometric Analysis of Panel Data (2005), and the choice between it and the random effects model is settled by the Hausman (1978) test.

Key highlights

Controls for all unobserved time-invariant heterogeneity across units without needing to measure it.
Consistent even when the unit effects are correlated with the regressors, unlike the random effects estimator.
Yields directly interpretable within-unit slopes that describe how the outcome changes as a predictor changes inside the same unit.

Intuition

This section is available to Pro members. Upgrade to Pro

How it works

This section is available to Pro members. Upgrade to Pro

When to use it

Use the fixed effects model with panel data — multiple units each observed over several time periods (more than one period is required) — and at least about 50 units. It fits when you want to estimate the effect of predictors while controlling for unobserved, time-invariant heterogeneity across units. It assumes strict exogeneity (regressors are uncorrelated with the errors) and is the right choice when a Hausman test favours fixed over random effects (p < 0.05); errors should be homoscedastic or clustered standard errors should be used. It is not suitable with a single time period, with too few units, or when endogeneity remains after controlling for fixed effects.

Strengths & limitations

Strengths

Controls for all unobserved time-invariant heterogeneity across units without needing to measure it.
Consistent even when the unit effects are correlated with the regressors, unlike the random effects estimator.
Yields directly interpretable within-unit slopes that describe how the outcome changes as a predictor changes inside the same unit.

Limitations

Cannot estimate the effect of time-invariant predictors, since the within transformation removes them along with the fixed effect.
With a single time period (T = 1) the within transformation is undefined and the model collapses to cross-sectional OLS.
With too few units (n < 50) the fixed effects estimator suffers from the incidental parameters problem and becomes unreliable.
Strict exogeneity is required; if endogeneity remains, fixed effects alone is insufficient and an instrumental-variable or GMM approach is needed.

Common pitfalls

This section is available to Pro members. Upgrade to Pro

Applications

This section is available to Pro members. Upgrade to Pro

Frequently asked

How do I choose between fixed and random effects?

Run a Hausman test. If it rejects the null (p < 0.05), the unit effects are correlated with the regressors and the random effects estimator is inconsistent, so fixed effects should be preferred. If it does not reject, random effects is the more efficient choice.

Why can't fixed effects estimate time-invariant variables?

The within transformation subtracts each unit's own time-average, which removes anything constant for that unit — including the fixed effect and any time-invariant predictor. Their coefficients are therefore not identified. If such variables are central, consider the random effects model instead.

What standard errors should I report?

Errors are usually correlated within a unit across time, so cluster-robust standard errors clustered by entity are recommended; default standard errors would understate the true uncertainty.

What if endogeneity remains after fixed effects?

Fixed effects only removes time-invariant unobserved heterogeneity. If a regressor is still correlated with the idiosyncratic error, the estimates are biased and an instrumental-variable or system-GMM approach is required.

Sources

1.
Hausman, J. A. (1978). Specification Tests in Econometrics. Econometrica, 46(6), 1251–1271.
DOI 10.2307/1913827
2.
Baltagi, B. H. (2005). Econometric Analysis of Panel Data (3rd ed.). Wiley.
ISBN 978-0470014561

You have read it. What now?

Ask about this method Similar methods

Cite this page

ScholarGate. (2026, June 1). Fixed Effects Panel Model. ScholarGate. https://scholargate.app/econometrics/fixed-effects-panel