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Extended regression models

• Combine endogeneity, Heckman-style selection, and treatment effects
• Linear regression
• Random effects in one or all equations
• Exogenous or endogenous regressors
• Exogenous or endogenous treatment assignment
  • Binary treatment–untreated/treated
  • Ordinal treatment levels–0 doses, 1 dose, 2 doses, etc.
• Endogenous selection using probit or tobit
• All standard postestimation command available, including predict and margins

Linear mixed models

• Multilevel random effects
• BLUP estimation
• Residual-error structures for linear models
• Standard errors of BLUPs
• Multiple imputation
• Bayesian estimation
• Bayesian quantile models New

Endogeneity and simultaneous systems

• Two-stage least-squares regression
• LIML estimation
• GMM estimation
• Instrumental variables
• Two-stage least-squares regression with high-dimensional fixed effects New
• Tests of instrumental relevance
• Tests of overidentifying restrictions
• Tests and confidence intervals robust to weak instruments New
• Control-function regression New
• Three-stage least-squares regression
• Linear constraints within and across equations
• Finite mixture models
• Linear regression with endogenous regressors, treatment effects, and sample selection
• Robust (Huber/White/sandwich) and cluster–robust standard errors
• Finite mixture models

Instrumental-variables quantile regression

• Inverse quantile regression estimator
• Smoothed estimating equations estimator
• Simultaneously estimate at different quantiles
• Hypothesis testing of endogenous effects
• Confidence intervals robust to weak instruments

Seemingly unrelated regressions

• Robust (Huber/White/sandwich) and cluster–robust standard errors New
• Linear constraints within and across equations

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
• Interaction plots
• Graphs of margins and marginal effects

Contrasts

• Analysis of main effects, simple effects, interaction effects, partial
interaction effects, and nested effects
• Comparisons against reference groups, of adjacent levels, or against
the grand mean
• Orthogonal polynomials
• Helmert contrasts
• Custom contrasts
• ANOVA-style tests
• Contrasts of nonlinear responses
• Multiple-comparison adjustments
• Balanced and unbalanced data
• Contrasts in odds-ratio metric
• Contrasts of means, intercepts, and slopes
• Graphs of contrasts

Pairwise comparisons

• Compare estimated means, intercepts, and slopes
• Compare marginal means, intercepts, and slopes
• Balanced and unbalanced data
• Nonlinear responses
• Multiple-comparison adjustments: Bonferroni, Šidák, Scheffé, Tukey HSD, Duncan, and Student–Newman–Keuls adjustments
• Group comparisons that are significant
• Graphs of pairwise comparisons

Additional resources

• Base Reference Manual
• Extended Regression Models Reference Manual
• Microeconometrics Using Stata, Second Edition, Volumes I and II by A. Colin Cameron and Pravin K. Trivedi
• Interpreting and Visualizing Regression Models Using Stata, Second Edition by Michael N. Mitchell