Binary choice

• Functional forms: probit, logit, extreme value (complementary log log), Gompertz, Burr, linear
• Proportions data: minimum chi-squared and MLE
• Marginal effects
• Standard errors by delta method
• Effects for dummy variables
• Effects evaluated at means and specified configurations
• Marginal effects by strata
• Numerous fit measures - tabulations of predictions
• Predicted probabilities: adjustable threshold for predictions
• Choice based sampling corrections
• Robust covariance matrices, cluster, sandwich
• Weights
• Linear restrictions: impose or test
• LM tests for specifications
• Heteroscedasticity
• Missing variables
• LR, Wald tests for restrictions
• Heteroscedastic probit or logit models
• ML estimation
• LM tests
• Marginal effects
• Two step estimation: use previous results or pass results to another estimator
• Semiparametric
• Klein and Spady
• Maximum score
• Nonparametric regression
• Panel data
• Random effects - quadrature or simulation
• Unconditional fixed effects one and two way (fit up to 20,000 dummy variable coefficients)
• Random parameters
• Latent class
• Conditional logit fixed effects
• LR and LM tests for effects

Bivariate probit

• Individual or proportions data
• Marginal effects
• Predictions
• Partial observability models (Abowd, Poirier, Meng/Schmidt)
• Choice based sampling
• Restrictions, LM and LR
• Sample selection model
• Panel data, random parameters, random effects
• Simultaneous equations

Multivariate probit

• Up to 20 equations - GHK simulator
• Marginal effects
• Restrictions and tests of restrictions
• Sample selection model