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
Probit*
Logistic regression*
Complementary log-log regression*
Ordered logistic regression*
Ordered probit regression*
Multinomial logistic regression*
Interval regression
Tobit
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
Im–Pesaran–Shin
Levin–Lin–Chu
Hadri
Breitung
Fisher-type (combining p-values)
Harris–Tzavalis
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
