Linear regression 
Watch Fitting and interpreting regression models tutorials
Censored outcomes
- Interval censored (such as income reported in ranges)
- Tobit model
- Correlated data corrections to standard errors
- Heteroskedastic consistent standard errors
- Model heteroskedasticity
- Predictions
- Outcome in the absence of censoring
- Outcome conditional on being in the censoring interval
- Outcome with censoring imposed
- Probability of censoring
- Finite mixture models
- Bayesian estimation
- Interval regression with endogenous regressors, treatment effects, and sample selection
Sample-selection linear models
- Maximum likelihood and Heckman's two-step estimation
- Robust, cluster–robust, bootstrap, and jackknife standard errors
- Linear constraints
- Combine with endogenous regressors and treatment effects
Hurdle models
- Linear and exponential
- Lower or upper boundary values
- Robust, cluster—robust, bootstrap, and jackknife standard errors
Stochastic frontier models
- Production and cost frontiers
- Half-normal, exponential, and truncated-normal distributions
- Modeling of conditional heteroskedasticity
Quantile regression
- Median regression
- Least absolute deviations (LAD)
- Regression of any quantile
- Interquantile range regression
- Standard errors
- Koenker and Bassett
- Robust — choose bandwidth and kernel
- Bootstrap
- Instrumental variables New
- Multiple imputation
- Bayesian estimation StataNow
Fractional polynomial regression
- Support for a wide variety of models
- Component-plus-residual plots
- Support for zero-inflated regressors
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
Endogeneity and simultaneous systems
- Two-stage least-squares regression
- LIML estimation
- GMM estimation
- Instrumental variables
- Tests of instrumental relevance
- Tests of overidentifying restrictions
- Tests and confidence intervals robust to weak instruments StataNow
- 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 New
- 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
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
- Interaction plots
- Graphs of margins and marginal effects
Watch Introduction to margins in Stata tutorials
Watch Profile plots and interaction plots in Stata tutorials
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
See
tests, predictions, and effects.
See New in Stata 18 to learn about what was added in Stata 18.
