Linear regression

• 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
• Constraints
• Instrumental variables
• Difference-in-differences estimation New
• Heteroskedastic regression
• Truncated regression
• Errors in variables
• Multivariate regression
• 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
• Tests for structural breaks
  • Unknown break point
  • Known break points
  • Cumulative sum test for stability of coefficients
• ARCH LM test
• Moran's test for spatial dependence
• Diagnostic plots
  • Added variable (leverage) plot
  • Component plus residual plot
  • Leverage vs. squared residual plot
  • Residual vs. fitted plot
  • Residual vs. predictor
• Effect sizes
  • Eta-squared—η ²
  • Omega-squared—ω²
  • Confidence intervals
• Fixed- and random-effects models for panel data
• Traditional, robust (Huber/White/sandwich), cluster–robust, bootstrap, or jackknife standard errors
• Robust regression
• Graph predictions and confidence intervals
• Newey–West estimator of variance
• Variance-weighted least squares
• GLM
• GLS for cross-sectional time-series data
• Multiple imputation
• Finite mixture models
• Bayesian estimation

Censored outcomes

• Interval censored (such as income reported in ranges)
• Tobit model
• Correlated data corrections to standard errors
• Heteroskedastic consistent standard errors
• Model heteroskedasticity
• Predictions
• Outcome in the absence of censoring
• Outcome conditional on being in the censoring interval
• Outcome with censoring imposed
• Probability of censoring
• Finite mixture models
• Bayesian estimation
• Interval regression with endogenous regressors, treatment effects, and sample selection

Sample-selection linear models

• Maximum likelihood and Heckman's two-step estimation
• Robust, cluster–robust, bootstrap, and jackknife standard errors
• Linear constraints
• Combine with endogenous regressors and treatment effects

Hurdle models

• Linear and exponential
• Lower or upper boundary values
• Robust, cluster—robust, bootstrap, and jackknife standard errors

Stochastic frontier models

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

Quantile regression

• Median regression
• Least absolute deviations (LAD)
• Regression of any quantile
• Interquantile range regression
• Standard errors
  • Koenker and Bassett
  • Robust — choose bandwidth and kernel
  • Bootstrap
• Multiple imputation

Fractional polynomial regression

• Support for a wide variety of models
• Component-plus-residual plots
• Support for zero-inflated regressors