Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/193375 
Erscheinungsjahr: 
2019
Schriftenreihe/Nr.: 
IZA Discussion Papers No. 12081
Verlag: 
Institute of Labor Economics (IZA), Bonn
Zusammenfassung: 
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.
Schlagwörter: 
lasso2
cvlasso
rlasso
lasso
elastic net
square-root lasso
cross-validation
JEL: 
C53
C55
C87
Dokumentart: 
Working Paper

Datei(en):
Datei
Größe
784.51 kB





Publikationen in EconStor sind urheberrechtlich geschützt.