Iassopack : model selection and prediction with regularized regression in stata / Achim Ahrens (The Economic and Social Research Institute), Christian B. Hansen (University of Chicago), Mark E. Schaffer (Heriot-Watt University and IZA) ; IZA Institute of Labor Economics
VerfasserAhrens, Achim ; Hansen, Christian Bailey ; Schaffer, Mark E.
KörperschaftForschungsinstitut zur Zukunft der Arbeit
ErschienenBonn, Germany : IZA Institute of Labor Economics, January 2019
Elektronische Ressource
Umfang1 Online-Ressource (52 Seiten) : Diagramme
SerieDiscussion paper ; no. 12081
 Das Dokument ist öffentlich zugänglich im Rahmen des deutschen Urheberrechts.
Iassopack [0.76 mb]
Verfügbarkeit In meiner Bibliothek
Zusammenfassung (Englisch)

This article introduces lassopack, a suite of programs for regularized regression in Stata. lassopack implements lasso, square-root lasso, elastic net, ridge regression, adaptive lasso and post-estimation OLS. The methods are suitable for the high-dimensional setting where the number of predictors p may be large and possibly greater than the number of observations, n. We offer three different approaches for selecting the penalization ("tuning") parameters: information criteria (implemented in lasso2), K-fold cross-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 panel data (rlasso). We discuss the theoretical framework and practical considerations for each approach. We also present Monte Carlo results to compare the performance of the penalization approaches.