Hypothesis testing
Wald test for linear constraints
Wald test (delta method) for nonlinear constraints
Likelihood-ratio test after any ML estimation
Bonferroni, Holm, and Šidák adjustments for multiple comparisons
Generalized testing
Ability to combine separate estimates into one combined estimate
Robust covariance matrix of combined estimates
Tests of linear and nonlinear combinations of estimates across fitted models
Point estimates and confidence intervals of linear and nonlinear combinations of estimates across fitted models
Predictions
Ability to obtain predicted values after all estimation commands
Predictor types that are tightly coupled to the estimation command
Default predicted value that is most relevant to the fitted model
Generalized predictions
Linear and nonlinear combinations of
Standard predictions
Equation index values
Estimated coefficients
Data
Inferential statistics for generalized predictions:
Point estimates
Standard errors
Variances
Wald test statistics
Significance levels
Pointwise confidence intervals
Postestimation statistics
Estimation sample summary statistics
Akaike and Bayesian information criteria
Covariance matrix analysis
Linear and nonlinear combinations of coefficients
Point estimates
Standard errors
Confidence intervals
Tests of significance
Covariances of transformations
Support for survey and clustered data
Effect sizes
Comparison of means
Cohen’s d
Hedge’s g
Glass’s Δ
Point/biserial correlation
Confidence intervals
Variance explained by regression and ANOVA
Eta-squared—η2
Omega-squared—ω2
Confidence intervals
Watch A tour of effect sizes.
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
Pairwise comparisons of margins
Profile 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
Postestimation Selector
View and run all postestimation features for your command
Automatically updated as estimation commands are run
Watch Postestimation Selector.
Forecast models
Combine results from multiple estimation commands
Specify identities and declare exogenous variables
Obtain dynamic and static forecasts
Use simulation methods to obtain predicition intervals
Specify alternative scenarios and perform “what-if” analyses
Watch Tour of forecasting in Stata.
Hausman test
Test the independence of irrelevant alternatives (IIA) after
Multinomial logit
Conditional logistic regression
Test exogeneity or overidentifying restrictions for
Two-stage least squares (2SLS)
Three-stage least squares (3SLS)
Specification link test for single-equation models
Save and restore estimation results
Save estimation results to disk
Compare models
Restore and perform predictions
Restore and perform tests
Additional resources