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Posts Tagged ‘lasso’

Using the lasso for inference in high-dimensional models

Why use lasso to do inference about coefficients in high-dimensional models?

High-dimensional models, which have too many potential covariates for the sample size at hand, are increasingly common in applied research. The lasso, discussed in the previous post, can be used to estimate the coefficients of interest in a high-dimensional model. This post discusses commands in Stata 16 that estimate the coefficients of interest in a high-dimensional model. Read more…

An introduction to the lasso in Stata

Why is the lasso interesting?

The least absolute shrinkage and selection operator (lasso) estimates model coefficients and these estimates can be used to select which covariates should be included in a model. The lasso is used for outcome prediction and for inference about causal parameters. In this post, we provide an introduction to the lasso and discuss using the lasso for prediction. In the next post, we discuss using the lasso for inference about causal parameters. Read more…