Is your response binary (for example, employed or unemployed), ordinal (education level), count (number of children), or censored (ticket sales in an existing venue)? Stata has maximum likelihood estimators—logistic, probit, ordered probit, multinomial logit, Poisson, tobit, and many others—that estimate the relationship between such outcomes and their determinants. A vast array of tools is available to analyze such models. Predict outcomes and their confidence intervals. Test equality of parameters or any linear or nonlinear combination of parameters. And much more.


LOGISTIC/LOGIT REGRESSION

  • Basic (dichotomous) ML logistic regression with influence statistics
  • Fit diagnostics and ROC curve
  • Classification table and sensitivity-versus-specificity graph
  • Complementary log-log regression
  • Skewed logistic regression
  • Grouped-data logistic regression
  • GLM for the binomial family
  • Robust, cluster–robust, bootstrap, and jackknife standard errors
  • Linear constraints
  • Multiple imputation
  • Bayesian estimation
  • Finite mixture models

 CONDITIONAL LOGISTIC REGRESSION

  • Conditional fixed-effects logit models (m:k matching) with exact likelihood (no limit on panel size)
  • For matched case–control groups
  • 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

FRACTIONAL REGRESSION

  • Fractional logistic regression
  • Beta regression
  • Fractional probit regression
  • Heteroskedastic fractional probit regression
  • Fractional probit regression with endogenous regressors
  • Bayesian estimation
  • Finite mixture models

 

ORDINAL REGRESSION MODELS

  • Ordered logistic (proportional-odds model)
  • Ordered probit
  • Heteroskedastic ordered probit
  • Zero-inflated ordered logit regression
  • Zero-inflated ordered probit regression
  • Robust, cluster–robust, bootstrap, and jackknife standard errors
  • Linear constraints
  • Bayesian estimation
  • Finite mixture models

 

TOBIT/CENSORED REGRESSION

  • Lower and upper limits of censoring
  • Specify censoring points that vary by observation
  • 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
  • Linear constraints
  • Bayesian estimation
  • Finite mixture models

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
  • Bayesian estimation
  • Finite mixture models

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
  • Poisson with sample selection
  • 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

 

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 ORDINAL MODELS

  • Zero-inflated ordered logit
  • Zero-inflated ordered probit
  • Robust, cluster–robust, bootstrap, and jackknife standard errors
  • Linear constraints
  • Predictions of marginal probabilities of levels, joint probabilities of levels and susceptibility, probability of susceptibility, probability of susceptibility, linear prediction, and more
  • Predictions of marginal probabilities of levels, joint probabilities of levels and participation, probability of participation, probability of nonparticipation, linear prediction, and more
  • Bayesian estimation

 

TRUNCATED COUNT MODELS

  • Zero-truncated, left-truncated, right-truncated, interval-truncated Poisson
  • Zero-truncated and left-truncated negative binomial
  • Truncation varying by observation

 

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  • 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
  • Random effects in one or all equations
  • 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

 

CHOICE MODELS

  • McFadden’s choice model
  • Mixed logit model
  • Panel-data mixed logit
  • Multinomial probit model
  • Nested logit model
  • Rank-ordered probit model
  • Rank-ordered logit model
  • Alternative-specific and case-specific variables
  • Advanced inference using margins

MULTINOMIAL LOGISTIC REGRESSION

  • Predicted probabilities of each outcome
  • 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
  • Robust, cluster–robust, bootstrap, and jackknife standard errors
  • Linear constraints
  • Bayesian estimation
  • Finite mixture models

MULTINOMIAL PROBIT REGRESSION

  • Predicted probabilities of each category
  • 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 marginal and bivariate probabilities, probabilities of levels conditional on selection or no selection, selection probability, linear prediction, and more
  • Bayesian estimation

SAMPLE SELECTION FOR POISSON REGRESSION

  • Robust, cluster—robust, bootstrap, and jackknife standard errors
  • Linear constraints
  • Predictions of number of events, incidence rate, probability of selection, linear prediction, and more

 

STEREOTYPE LOGISTIC REGRESSION

  • Predictions of probabilities of outcomes
  • Robust, cluster–robust, bootstrap, and jackknife standard errors
  • Linear constraints

RELATIVE EXCESS RISK DUE TO INTERACTION

  • Additive models of relative risk
  • Relative excess risk due to interaction (RERI) statistic
  • Attributable proportion (AP)
  • Synergy index (SI)
  • Supports binomial generalized linear models; logistic, Poisson, and negative binomial regressions; and Cox and other survival models

POSTESTIMATION SELECTOR

  • View and run all postestimation features for your command
  • Automatically updated as estimation commands are run

 

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