Linear regression
Influence statistics and fit diagnostics
Ramsey regression specification-error test for omitted variables
Variance-inflation factors
Cook’s distance
COVRATIO
DFBETAs
DFITs
Diagonal elements of hat matrix
Residuals, standardized residuals, studentized residuals
Standard errors of the forecast, prediction, and residuals
Welsch distance
Constraints
Instrumental variables
Heteroskedastic regression
Truncated regression
Errors in variables
Tests for heteroskedasticity
Cook and Weisberg test
Szroetzer’s rank test
Information matrix test
Cameron and Trivedi’s decomposition
White’s test
Tests for autocorrelation
Durbin–Watson
Durbin–Watson d statistic
Breusch–Godfrey
Tests for structural breaks
Unknown break point
Known break points
Cumulative sum test for stability of coefficients
ARCH LM test
Moran’s test for spatial dependence
Diagnostic plots
Added variable (leverage) plot
Component plus residual plot
Leverage vs. squared residual plot
Residual vs. fitted plot
Residual vs. predictor
Effect sizes
Eta-squared—η 2
Omega-squared—ω2
Confidence intervals
Fixed- and random-effects models for panel data
Traditional, robust (Huber/White/sandwich), cluster–robust, bootstrap, or jackknife standard errors
Robust regression
Graph predictions and confidence intervals
Newey–West estimator of variance
Variance-weighted least squares
GLM
GLS for cross-sectional time-series data
Multiple imputation
Finite mixture models
Bayesian estimation
Watch Heteroskedastic linear regression.
Watch Tests for multiple breaks in time series.
Watch Simple linear regression.
Watch Instrumental-variables regression.
Watch A tour of effect sizes.
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
Modelling 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
Extended regression models
Combine endogeneity, Heckman-style selection, and treatment effects
Linear regression
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
Watch Extended regression models (ERMs)
Linear mixed models
Multilevel random effects
BLUP estimation
Residual-error structures for linear models
Standard errors of BLUPs
Bayesian estimation
Fractional polynomial regression
Support for a wide variety of models
Component-plus-residual plots
Support for zero-inflated regressors
Endogeneity and simultaneous systems
Two-stage least-squares regression
Poisson with endogenous regressors
LIML estimation
GMM estimation
Instrumental variables
Tests of instrumental relevance
Tests of overidentifying restrictions
Three-stage least-squares regression
Linear constraints within and across equations
Finite mixture models
Linear regression with endogenous regressors, treatment effects, and sample selection
Seemingly unrelated regressions
Linear constraints within and across equations
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
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
A single continuous variable
Interactions of categorical variables
Interactions of categorical and continuous variables
Interactions of two continuous variables
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