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 single 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
  • Variance
  • Wald test statistics
  • Significance levels
  • Pointwise confidence intervals

Postestimation statistics

• Estimation sample summary statistics
• Akaike and Bayesian information criteria
• Covariance matrix analysis

• Predicted values or probabilities
• Predictions adjusted to set levels of regressors
• Predictions adjusted to set levels of covariates
• Predictions adjusted to set levels of terms

Hausman test

• Test the independence of irrelevant alternative (IIA) after
  • Multinomial logit
  • Conditional logistic regression
• Test exogeneity or overidentifying restrictions for
  • Two-stage least squares (2SLS)
  • Three-stage least squares (3SLS)

Point estimates for combinations of coefficients

• Odds ratios
• Standard errors
• Test statistics

• Marginal effects and elasticities
• Standard errors and confidence intervals
• Computation of effects at means or specified covariate values
• Computation of effects for any predicted statistic

Save and restore estimation results

• Point estimates
• Standard errors
• t and Z statistics
• p-values
• Confidence intervals
• Covariances between combinations
• Support for survey and clustered data