Binary, fractional, count, and limited outcomes

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Binary, fractional, count, and limited outcomes

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

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

Watch Logistic regression tutorials

Fitting and interpreting regression models: Logistic regression with categorical predictors
Fitting and interpreting regression models: Logistic regression with continuous predictors
Fitting and interpreting regression models: Logistic regression with continuous and categorical predictors

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

Watch Regression models for fractional data.

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

Watch Heteroskedastic ordered probit models.
Watch Zero-inflated ordered logit model.

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

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 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

Watch Zero-inflated ordered probit.
Watch Zero-inflated ordered logit model.

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
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

Watch Extended regression models (ERMs).

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
Control-function probit regression New
Grouped-data probit regression
Heteroskedastic probit regression
Robust, cluster–robust, bootstrap, and jackknife standard errors
Linear constraints
Bayesian estimation
Finite mixture models

Watch Probit regression tutorials

Probit regression with categorical covariates
Probit regression with continuous covariates
Probit regression with categorical and continuous covariates

Watch Control-function linear and probit models New

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

Watch Poisson with sample selection.

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