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Binary, fractional, count, and limited outcomes

Logistic/logit regression

  • Basic (dichotomous) ML logistic regression
  • with influence statistics
  • 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
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 Updated
  • 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

Multinomial logistic regression

  • Robust, cluster–robust, bootstrap, and jackknife standard errors
  • Linear constraints

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 Updated
  • Robust, cluster–robust, bootstrap, and jackknife standard errors
  • Linear constraints

Fractional regression New

  • Beta regression
  • Fractional probit regression
  • Fractional logistic regression
  • Heteroskedastic fractional probit regression

Ordinal regression models

  • Ordered logistic (proportional-odds model)
  • Ordered probit
  • Robust, cluster–robust, bootstrap, and jackknife standard errors
  • Linear constraints

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

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

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

Censored Poisson regression New

  • 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

Left-truncated count models

  • Zero-truncated Poisson
  • Zero-truncated negative binomial
  • Left-truncated Poisson
  • 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

Nested logit Updated

  • 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 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

Heckman selection models

  • Two-step and maximum likelihood (ML)
  • Robust, cluster–robust, bootstrap, and jackknife standard errors (ML only)
  • Bootstrap and jackknife standard errors (two-step)
  • Linear constraints (ML only)
  • Predictions available for Mills’ ratio, expected value, conditional expected value, probability of selection, nonselection hazard, and more

Heckman 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

Heckman 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 production, and more

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 New

  • 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 New
  • 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

See tests, predictions, and effects.

See New in Stata 14 for more about what was added in Stata 14.

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