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纵向数据/面板数据
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
Specification tests
- Hausman specification test
- Breusch and Pagan Lagrange multiplier test for random effects
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
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
Multilevel mixed-effects models 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-variables estimators
- Amemiya–MaCurdy instrumental-variables estimators
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
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
Swamy’s random-coefficients regression Panel-data line plots
- Graphs by panel
- Overlaid panels
GEE estimation of generalized 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
Population-averaged regression
- Complementary log-log regression
- Logit regression
- Negative binomial regression
- Poisson regression
- Probit regression
- Linear models regression
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
- Contrasts of margins
- Pairwise comparisons of margins
- Profile plots
- Graphs of margins and marginal effects
Contrasts
- Analysis of main effects, simple effects, interaction effects, partial interaction effects, and nested effects
- Comparisons against reference groups, of adjacent levels, or against the grand mean
Orthogonal polynomials
- Helmert contrasts
- Custom contrasts
- ANOVA-style tests
- Contrasts of nonlinear responses
- Multiple-comparison adjustments
- Balanced and unbalanced data
- Contrasts in odds-ratio metric
- Contrasts of means, intercepts, and slopes
- Graphs of contrasts
- Interaction plots
Pairwise comparisons
- Compare estimated means, intercepts, and slopes
- Compare marginal means, intercepts, and slopes
- Balanced and unbalanced data
- Nonlinear responses
- Multiple-comparison adjustments: Bonferroni, ?idák, Scheffé, Tukey HSD, Duncan, and Student-Newman-Keuls adjustments
- Group comparisons that are significant
- Graphs of pairwise comparisons
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