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Panel/longitudinal data

Take full advantage of the extra information that panel data provide, while simultaneously handling the peculiarities of panel data. Study the time-invariant features within each panel, the relationships across panels, and how outcomes of interest change over time. Fit linear models or nonlinear models for binary, count, ordinal, censored, or survival outcomes with fixed-effects, random-effects, or population-averaged estimators. Fit dynamic models or models with endogeneity. And much more.

Linear fixed- and random-effects models

  • Linear model with panel-level effects and i.i.d. errors
  • Linear model with panel-level effects and AR(1) errors
  • GLS and ML estimators
  • Robust and cluster–robust standard errors
  • Multiple imputation
  • Bayesian estimation

Random-effects regression for binary, ordinal, and count-dependent variables

  • Probit *
  • Logistic regression *
  • Complementary log-log regression *
  • Ordered logistic regression *
  • Ordered probit regression *
  • Multinomial logistic regression *
  • Interval regression
  • Tobit
  • Poisson regression (Gaussian or gamma random-effects) *
  • Negative binomial regression
  • Bayesian estimation

*Robust standard errors

Conditional fixed-effects regression for binary and count-dependent variables

  • Logit regression
  • Poisson regression
  • Negative binomial regression

Two-stage least-squares panel-data estimators

  • Between-2SLS estimator
  • Within-2SLS estimator
  • Balestra–Varadharajan–Krishnakumar G2SLS estimator
  • Baltagi EC2SLS estimator
  • All with balanced or exogenously balanced panels
  • Robust and cluster–robust standard errors

Random-effects regression with sample selection New

    Random-effects extended regression models New

    • Combine endogeneity, Heckman-style selection, and treatment effects
    • Linear regression
    • 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 commands available, including predict and margins

    Regressors correlated with individual-level effects

    • Hausman–Taylor instrumental-variables estimators
    • Amemiya–MaCurdy instrumental-variables estimators
    • Robust and cluster–robust standard errors

    Panel-corrected standard errors (PCSE) for linear cross-sectional models

      Swamy’s random-coefficients regression

        Stochastic frontier models

        • Time-invariant model
        • Time-varying decay model
        • Battese–Coelli parameterization of time effects
        • Estimates of technical efficiency and inefficiency

        Specification tests

        Panel-data unit-root tests

        • Im–Pesaran–Shin
        • Levin–Lin–Chu
        • Hadri
        • Breitung
        • Fisher-type (combining p-values)
        • Harris–Tzavalis

        Summary statistics and tabulations

        • Statistics within and between panels
        • Pattern of panel participation

        Panel-data line plots

        • Graphs by panel
        • Overlaid panels

        GEE estimation of generalized linear models (GLMs)

        Linear dynamic panel-data estimators

        • Arellano–Bond estimator
        • Arellano–Bover/Blundell–Bond system
        • Opening, closing, and embedded gaps
        • Serially correlated disturbances
        • Complete control over instrument list
        • Predetermined variables
        • Tests for autocorrelation and of overidentifying restrictions

        Random-effects parametric survival models

        • Weibull, exponential, lognormal, loglogistic, or gamma models
        • Robust and cluster–robust standard errors
        • Bayesian estimation

        Multilevel mixed-effects models

          Population-averaged regression

          • Complementary log-log regression
          • Generalized estimating equations
          • Logit regression
          • Negative binomial regression
          • Poisson regression
          • Probit regression
          • Linear models regression

          Stationarity tests

          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 Updated
          • Include polynomial terms
          • Perform contrasts of categories/levels
          Watch Introduction to Factor Variables in Stata tutorials

          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 resources

          See tests, predictions, and effects.

          See New in Stata 16 for more about what was added in Stata 16.

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