|
Logistic/logit regression
- Basic (dichotomous) ML logistic regression with influence statistics
- Fit diagnostics and ROC curve
- Robust, cluster–robust, bootstrap, and jackknife standard errors
- Skewed logistic regression
- Grouped-data logistic regression
Conditional logistic regression
- McFadden's choice model
- 1:1 and 1:k matching
- Conditional fixed-effects logit models (m:k matching) with exact likelihood (no limit on panel size)
- Robust, cluster–robust, bootstrap, and jackknife standard errors
- Linear constraints
Multinomial logistic regression
- Robust, cluster–robust, bootstrap, and jackknife standard errors
- Linear constraints
Ordinal regression models
- Ordered logistic (proportional-odds model)
- Ordered probit
- Robust, cluster–robust, bootstrap, and jackknife standard errors
Tobit regression and truncated regression
- Lower and upper limits of censoring
- Differing limits for each observation
- Predictions available for expected value, conditional expected value, censored expected value, and probability of censoring
- Endogenous regressors
- Bootstrap and jackknife standard errors for tobit regression
- Robust, cluster–robust, bootstrap, and jackknife standard errors for truncated regression
- Linear constraints
Interval or interval-censored regression
- Open and closed intervals
- Robust, cluster–robust, bootstrap, and jackknife standard errors for interval regression
- Bootstrap and jackknife standard errors for censored-normal regression
- Linear constraints
Poisson and negative-binomial regression
- Poisson goodness-of-fit tests
- Robust, cluster–robust, bootstrap, and jackknife standard errors
- Linear constraints
Rank-ordered logistic regression
- Plackett–Luce model, exploded logit, choice-based conjoint analysis
- Complete rankings of ordered outcome
- Incomplete rankings of ordered outcome
- Ties ("indifference")
- Robust or cluster–robust standard errors
Stereotype logistic regression
- Predictions of probabilities of outcomes
- Robust, cluster–robust, bootstrap, and jackknife standard errors
- Linear constraints
|
|
Nested logit
- Full maximum-likelihood estimation
- Up to eight nested levels
- Facilities to set up the data and display the tree structure
- Linear constraints, including constraints on inclusive value parameters
- Predictions available for utility functions, probabilities, conditional probabilities, and inclusive values
- Robust standard errors
Multinomial probit regression
- Alternative- and case-specific variables
- Homo- or heteroskedastic variances
- Various correlation structures, including user-specified
- Probabilities based on GHK simulator
- Robust, cluster–robust, bootstrap, and jackknife standard errors
- Linear constraints
Heckman selection models
- Two-step and full maximum likelihood
- Predictions available for Mills' ratio, expected value, conditional expected value, probability of selection, nonselection hazard, and more
- Robust, cluster–robust, bootstrap, and jackknife standard errors (maximum likelihood estimator only)
- Linear constraints
Heckman selection with a binary outcome
- Predictions available for probability of binary outcome, all four combinations of outcome and selection, probability of selection, conditional probability of outcome, and more
- Robust, cluster–robust, bootstrap, and jackknife standard errors (maximum likelihood estimator only)
- Linear constraints
Zero-inflated models
-
- Zero-inflated Poisson
- Zero-inflated negative binomial
- Robust, cluster–robust, bootstrap, and jackknife standard errors
- Linear constraints
Zero-truncated models
- Zero-truncated Poisson
- Zero-truncated negative binomial
- Robust, cluster–robust, bootstrap, and jackknife standard errors
- Linear constraints
Treatment-effects model
- Fitted values and their standard error (SE)
- Expected value given treatment or nontreatment and their SEs
- Probability of treatment and its SE
- Robust, cluster–robust, bootstrap, and jackknife standard errors
- Linear constraints
Marginal effects
- Marginal effects and elasticities
- Standard errors and confidence intervals
- Computation at means or specified covariate values
- Computation for any predicted statistic
Linear and nonlinear combinations
- Combinations of coefficients
- Combinations of predictions
- See Model testing and post-estimation support
|