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Lasso New
With Stata's lasso and elastic net features, you can perform
model selection and prediction for your continuous, binary,
and count outcomes. Want to estimate effects and test coefficients?
With cutting-edge inferential methods, you can make inferences
for variables of interest while lassos select control variables for
you. You can even account for endogenous covariates.
Lasso for prediction and model selection
- Lasso
- Elastic net
- Square-root lasso
- Continuous, binary, and count outcomes
- Effect estimates for covariates of interest
- Coefficients, SEs, tests, confidence intervals
- Lasso selects control variables
- Robust to model-selection mistakes by lasso
- Double selection
- Partialing out
- Cross-fit partialing out
- Double machine learning (DML1 and DML2)
- Linear, logit, and Poisson regression
- Endogenous covariates in linear models
Watch Lasso for inference.
Data preparation
- Divide data into random samples
- Manage large lists of variables
- Create names for referring to
- All continuous variables
- All categorical variables
- Your own variable groupings
Selection methods
- Cross-validation
- Adaptive lasso
- Plugin iterative formula
- Select your own
Evaluate selected model
- Graphs
- Cross-validation function plot
- Coefficient paths
- Goodness of fit
- Mean squared error of prediction
- In-sample and out-of-sample R-squareds
- Deviance
- In-sample and out-of-sample deviance ratios
- BIC
- Relative L1-norm of coefficients
- Relative L2-norm squared of coefficients
- Estimated coefficients
- Penalized
- Postselection
Additional resources
See New in Stata 16 for more about what was added in Stata 16.
