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
Tests
- Hausman specification test
- Breusch and Pagan Lagrange multiplier test for random effects
Arellano–Bond linear, dynamic panel-data estimator
- One step, one-step robust, two step
- Exogenously unbalanced panels
- Opening, closing, and embedded gaps
- Predetermined covariates
- Full instrument list or pared-down version
Panel-corrected standard errors (PCSE) for linear cross-sectional models
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
Stochastic frontier models
- Time-invariant model
- Time-varying decay model
- Battese–Coelli parameterization of time effects
- Estimates of technical efficiency and inefficiency
Regressors correlated with individual-level effects
- Hausman–Taylor instrumental variable estimators
- Amemiya–MaCurdy instrumental variable estimators
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Multilevel mixed-effects models
GEE estimation of general linear models (GLMs)
- 6 distribution families
- 9 links
- 7 correlation structures
- Specific models include:
- probit model with panel-correlation structure
- Poisson model with panel-correlation structure
Summary statistics and tabulations
- Statistics within and between panels
- Pattern of panel participation
Random-effects regression for binary and count-dependent variables
- Interval regression
- Tobit
- Probit
- Logistic regression
- Complementary log-log regression
- Poisson regression (Gaussian random-effects)
- Poisson regression (gamma random-effects)
- Negative binomial regression
- Linear parameter constraints
Conditional fixed-effects regression for binary and count-dependent variables
- Logit regression
- Poisson regression
- Negative binomial regression
Population-averaged regression
- Complementary log-log regression
- Logit regression
- Negative binomial regression
- Poisson regression
- Probit regression
- Linear models regression
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