Linear fixed- and random-effects models

Linear model with panel-level effects and i.i.d. errors

Linear model with panel-level effects and AR(1) errors

GLS and ML estimators

Robust and cluster–robust standard errors

Multiple imputation


Random-effects regression for binary, ordinal, and count-dependent variables


Logistic regression*

Complementary log-log regression*

Ordered logistic regression*

Ordered probit regression*

Multinomial logistic regression*

Interval regression


Poisson regression (Gaussian or gamma random-effects)*

Negative binomial regression

*Robust standard errors


Conditional fixed-effects regression for binary and count-dependent variables

Logit regression

Poisson regression

Negative binomial regression


Random-effects parametric survival models

Weibull, exponential, lognormal, loglogistic, or gamma models

Robust and cluster–robust standard errors


Two-stage least-squares panel-data estimators

Between-2SLS estimator

Within-2SLS estimator

Balestra–Varadharajan–Krishnakumar G2SLS estimator

Baltagi EC2SLS estimator

All with balanced or exogenously balanced panels

Robust and cluster–robust standard errors


Regressors correlated with individual-level effects

Hausman–Taylor instrumental-variables estimators

Amemiya–MaCurdy instrumental-variables estimators

Robust and cluster–robust standard errors


Panel-corrected standard errors (PCSE) for linear cross-sectional models


Swamy’s random-coefficients regression


Stochastic frontier models

Time-invariant model

Time-varying decay model

Battese–Coelli parameterization of time effects

Estimates of technical efficiency and inefficiency


Specification tests

Hausman specification test

Breusch and Pagan Lagrange multiplier test for random effects


Panel-data unit-root tests





Fisher-type (combining p-values)



Summary statistics and tabulations

Statistics within and between panels

Pattern of panel participation


Panel-data line plots

Graphs by panel

Overlaid panels


GEE estimation of generalized linear models (GLMs)

Six distribution families

Nine links

Seven correlation structures

Specific models include:

Probit model with panel-correlation structure

Poisson model with panel-correlation structure


Linear dynamic panel-data estimators

Arellano–Bond estimator

Arellano–Bover/Blundell–Bond system

Opening, closing, and embedded gaps

Serially correlated disturbances

Complete control over instrument list

Predetermined variables

Tests for autocorrelation and of overidentifying restrictions


Population-averaged regression

Complementary log-log regression

Logit regression

Negative binomial regression

Poisson regression

Probit regression

Linear models regression


Postestimation Selector

View and run all postestimation features for your command

Automatically updated as estimation commands are run


Factor variables

Automatically create indicators based on categorical variables

Form interactions among discrete and continuous variables

Include polynomial terms

Perform contrasts of categories/levels


Marginal analysis

Estimated marginal means

Marginal and partial effects

Average marginal and partial effects

Least-squares means

Predictive margins

Adjusted predictions, means, and effects

Works with multiple outcomes simultaneously

Contrasts of margins

Pairwise comparisons of margins

Profile plots

Graphs of margins and marginal effects


Additional resources

Longitudinal-Data/Panel-Data Reference Manual

Panel Data Analysis Using Stata public training course


*New in Stata 14