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

Bayesian estimation

 

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

Bayesian estimation

*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

Bayesian estimation

Watch Panel-data survival models in Stata.

 

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

 

Multilevel mixed-effects models

Watch Multilevel models for survey data in Stata.

 

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

 

Stationarity tests

Panel-data unit-root tests

Im–Pesaran–Shin

Levin–Lin–Chu

Hadri

Breitung

Fisher-type (combining p-values)

Harris–Tzavalis

Cointegration tests for nonstationary process

Kao, Pedroni, or Westerlund tests

Include panel-specific means or panel-specific time trends

Watch Panel-data cointegration tests.

 

Postestimation Selector

View and run all postestimation features for your command

Automatically updated as estimation commands are run

Watch Postestimation Selector.

 

Factor variables

Automatically create indicators based on categorical variables

Form interactions among discrete and continuous variables

Include polynomial terms

Perform contrasts of categories/levels

Watch Introduction to Factor Variables in Stata tutorials

 

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

Watch Introduction to margins in Stata tutorials
Watch Profile plots and interaction plots in Stata tutorials

 

Additional resources

In the spotlight: Meet Stata’s new xtmlogit

NetCourse 471: Introduction to Panel Data Using Stata