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

Contrasts of margins

Pairwise comparisons of margins

Profile plots

Graphs of margins and marginal effects

 

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

In the spotlight: Margins of predicted outcomes

In the spotlight: marginsplot