Regression

• Ordinary, constrained, instrumental variables, censored, and errors in variables
• Influence statistics and fit diagnostics
  • Ramsey regression specification-error test for omitted variables
  • Variance-inflation factors
  • Cook’s distance
  • COVRATIO
  • DFBETAs
  • DFITs
  • Diagonal elements of hat matrix
  • Residuals, standardized residuals, studentized residuals
  • Standard errors of the forecast, prediction, and residuals
  • Welsch distance
• Tests for heteroskedasticity
  • Cook and Weisberg test
  • Szroetzer’s rank test
  • Information matrix test
  • Cameron and Trivedi’s decomposition
  • White’s test
• Tests for autocorrelation
  • Durbin–Watson
  • Durbin–Watson d statistic
  • Breusch–Godfrey
• ARCH LM test
• Diagnostic plots
  • Added variable (leverage) plot
  • Component plus residual plot
  • Leverage vs. squared residual plot
  • Residual vs. fitted plot
  • Residual vs. predictor
• Nested logit models
• Fixed- and random-effects models for panel data
• Traditional, robust (Huber/White/sandwich), cluster–robust, bootstrap, or jackknife standard errors
• Robust regression
• Graph estimates and confidence intervals
• Newey–West estimator of variance
• Variance-weighted least squares
• GLM
• GLS for cross-sectional time-series data
• List estimates and confidence intervals

Watch Simple Linear Regression in Stata tutorial.

Stochastic frontier models

• Production and cost frontiers
• Half-normal, exponential, and truncated-normal distributions
• Modelling of conditional heteroskedasticity

Quantile regression

• Median regression
• Least absolute deviations (LAD)
• Regression of any quantile
• Koenker and Bassett or bootstrapped standard errors

Linear mixed models

• Multilevel random effects
• BLUP estimation
• Residual-error structures for linear models
• Standard errors of BLUPs

Fractional polynomial regression

• Mean adjustment to variables
• Component-plus-residual plots

Simultaneous systems

• Three-stage least-squares regression
• Two-stage least-squares regression
• LIML estimation
• GMM estimation
• Tests of instrumental relevance
• Tests of overidentifying restrictions
• Linear constraints within and across equations
• Models with selection

Seemingly unrelated regressions

• Linear constraints within and across equations

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

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

Watch Introduction to Margins in Stata

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, Šidák, Scheffé, Tukey HSD, Duncan, and Student-Newman-Keuls adjustments
• Group comparisons that are significant
• Graphs of pairwise comparisons