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lassopack Model selection and prediction with regularized regression in Stata

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Document pages: 52 pages

Abstract: This article introduces lassopack, a suite of programs for regularizedregression in Stata. lassopack implements lasso, square-root lasso, elasticnet, ridge regression, adaptive lasso and post-estimation OLS. The methods aresuitable for the high-dimensional setting where the number of predictors $p$may be large and possibly greater than the number of observations, $n$. Weoffer three different approaches for selecting the penalization (`tuning )parameters: information criteria (implemented in lasso2), $K$-foldcross-validation and $h$-step ahead rolling cross-validation for cross-section,panel and time-series data (cvlasso), and theory-driven (`rigorous )penalization for the lasso and square-root lasso for cross-section and paneldata (rlasso). We discuss the theoretical framework and practicalconsiderations for each approach. We also present Monte Carlo results tocompare the performance of the penalization approaches.

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