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 (AIC, CAIC, AICc) 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
MARGINAL ANALYSIS
- Estimated marginal means
- Marginal and partial effects
- Average marginal and partial effects
- Least-squares means
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- 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
- A single categorical variable
- 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
FORECAST MODELS
- Combine results from multiple estimation commands
- Specify identities and declare exogenous variables
- Obtain dynamic and static forecasts
- Use simulation methods to obtain prediciction intervals
- Specify alternative scenarios and perform “what-if” analyses
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
BAYESIAN POSTESTIMATION
- Hypothesis testing
- Predictions
- Model comparison (Bayes factors)
- Model goodness of fit
- Bayesian dynamic forecast after VAR
- And more