Logistic/logit regression

Basic (dichotomous) ML logistic regression with influence statistic

Fit diagnostics and ROC curve

Classification table and sensitivity-versus-specificity graph

Skewed logistic regression

Grouped-data logistic regression

Robust, cluster–robust, bootstrap, and jackknife standard errors

Linear constraints

Multiple imputation

Bayesian estimation

Finite mixture models

 

Watch Logistic regression tutorials

 

Conditional logistic regression

Conditional fixed-effects logit models (m:k matching) with exact likelihood (no limit on panel size)

For matched case–control groups

McFadden’s choice model

1:1 and 1:k matching

Robust, cluster–robust, bootstrap, and jackknife standard errors

Linear constraints

Predictions for influence and lack-of-fit statistics and Pearson residuals

Bayesian estimation

 

 

Mixed logit regression

Also known as

Mixed multinomial logit models

Mixed discrete choice models

Discrete choice models with random coefficients

Random-effect and random-coefficient distributions

Normal

Correlated normal

Lognormal

Truncated normal

Uniform

Triangular

Robust and cluster–robust standard errors

Survey data support

 

 

Fractional regression

Beta regression

Fractional probit regression

Fractional logistic regression

Heteroskedastic fractional probit regression

Bayesian estimation

Finite mixture models

Watch Regression models for fractional data.

 

 

Ordinal regression models

Ordered logistic (proportional-odds model)

Ordered probit

Robust, cluster–robust, bootstrap, and jackknife standard errors

Linear constraints

Bayesian estimation

Finite mixture models

 

 

Tobit/censored regression

Lower and upper limits of censoring

Predictions available for expected value, conditional expected value, censored expected value, and probability of censoring

Endogenous regressors

Selection models

Random effects and random coefficients

Treatment effects (ATEs)

Multivariate models

Unobserved components

Endogenous switching models

Robust, cluster–robust, bootstrap, and jackknife standard errors

 

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

Robust, cluster–robust, bootstrap, and jackknife standard errors

Linear constraints

 

Truncated count models

Zero-truncated, left-truncated, right-truncated, interval-truncated Poisson

Zero-truncated and left-truncated negative binomial

Truncation varying by observation

Predict expected counts, incidence rates, and probabilities of counts

Robust, cluster–robust, bootstrap, and jackknife standard errors

Linear constraints

Bayesian estimation

Finite mixture models

 

Extended regression models

Combine endogeneity, Heckman-style selection, and treatment effects

Interval regression, including tobit

Probit regression

Ordered probit regression

Exogenous or endogenous treatment assignment

Binary treatment–untreated/treated

Ordinal treatment levels–0 doses, 1 dose, 2 doses, etc.

Endogenous selection using probit or tobit

All standard postestimation command available, including predict and margins

Watch Extended regression models (ERMs).

 

Interval regression

Open and closed intervals

Endogenous regressors

Selection models

Random effects and random coefficients

Treatment effects (ATEs)

Multivariate models

Unobserved components

Endogenous switching models

Robust, cluster–robust, bootstrap, and jackknife standard errors

Linear constraints

Bayesian estimation

Finite mixture models

 

 

Poisson and negative binomial regression

Predict expected counts, incidence rates, and probabilities of counts

Poisson goodness-of-fit tests

Poisson model with endogenous regressors

Robust, cluster–robust, bootstrap, and jackknife standard errors

Linear constraints

Bayesian estimation

Finite mixture models

 

 

Censored Poisson regression

Left, right, and interval censoring

Robust, cluster–robust, bootstrap, and jackknife standard errors

Linear constraints

Watch Censored Poisson regression.

Zero-inflated count models

Zero-inflated Poisson

Zero-inflated negative binomial

Predict expected counts, incidence rates, and probabilities of counts

Robust, cluster–robust, bootstrap, and jackknife standard errors

Linear constraints

Bayesian estimation

 

 

Zero-inflated ordered probit regression

Robust, cluster–robust, bootstrap, and jackknife standard errors

Linear constraints

Predictions of marginal probabilities of levels, joint probabilities of levels and participation, probability of participation, probability of nonparticipation, linear prediction, and more

Bayesian estimation

Watch Zero-inflated orderd probit.

 

Nested logit

Random-utilities maximization model

Full maximum-likelihood estimation

Up to eight nested levels

Facilities to set up the data and display the tree structure

Predictions available for utility functions, probabilities, conditional probabilities, and inclusive values

Robust, cluster–robust, bootstrap, and jackknife standard errors

Linear constraints, including constraints on inclusive-value parameters

 

Multinomial logistic regression

Alternative-specific and case-specific variables

Mixed multinomial logit regression

Robust, cluster–robust, bootstrap, and jackknife standard errors

Linear constraints

Bayesian estimation

Finite mixture models

 

Probit regression

Dichotomous outcome with ML estimates

Bivariate probit regression

Endogenous regressors

Grouped-data probit regression

Heteroskedastic probit regression

Rank-ordered with alternative-specific and case-specific variables

Robust, cluster–robust, bootstrap, and jackknife standard errors

Linear constraints

Bayesian estimation

Finite mixture models

 

Multinomial probit regression

Alternative-specific and case-specific variables Updated

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

 

Sample-selection models for continuous outcomes

Two-step (Heckman method) and maximum likelihood (ML)

Robust, cluster–robust, bootstrap, and jackknife standard errors

Bootstrap and jackknife standard errors

Linear constraints

Predictions available for Mills’ ratio, expected value, conditional expected value, probability of selection, nonselection hazard, and more

Bayesian estimation

 

Sample selection with a binary outcome

Robust, cluster–robust, bootstrap, and jackknife standard errors

Linear constraints

Predictions available for probability of binary outcome, all four combinations of outcome and selection, probability of selection, conditional probability of outcome, and more

Bayesian estimation

 

Sample selection for ordered probit

Robust, cluster–robust, bootstrap, and jackknife standard errors

Linear constraints

Predictions available for probability of binary outcome, all four combinations of outcome and selection, probability of selection, conditional probability of outcome, and more

Bayesian estimation

 

 

Sample selection for Poisson regression

Robust, cluster-robust, bootstrap, and jackknife standard errors

Linear constraints

Predictions available for marginal and bivariate probabilities, probabilities of levels conditional on selection or no selection, selection probability, linear production, and more

Watch Poisson with sample selection.

 

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”)

Prediction of probability that alternatives are ranked first

Robust, cluster–robust, bootstrap, and jackknife standard errors

 

Stereotype logistic regression

Predictions of probabilities of outcomes

Robust, cluster–robust, bootstrap, and jackknife standard errors

Linear constraints

 

Postestimation Selector

View and run all postestimation features for your command

Automatically updated as estimation commands are run

Watch Postestimation Selector.

 

Factor variables

Automatically create indicators based on categorical variables

Form interactions among discrete and continuous variables

Include polynomial terms

Perform contrasts of categories/levels

 

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

Watch Introduction to margins in Stata tutorials
Watch Profile plots and interaction plots in Stata tutorials

 

Additional resource

Regression Models for Categorical Dependent Variables using Stata, Third Edition

Extended Regression Models Reference Manual