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Longitudinal data/panel 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

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) * Updated
  • Negative binomial regression

*Robust standard errors

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

  • Logit regression
  • Poisson regression
  • Negative binomial regression

Random-effects parametric survival models New

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

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 New

Multilevel mixed-effects models Updated

    Regressors correlated with individual-level effects

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

    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

        Population-averaged regression

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

        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
        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 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 resources

        See tests, predictions, and effects.

        Watch a video overview of longitudinal and panel-data estimators and features added in Stata 13.

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





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