Premium
Lasso for Instrumental Variable Selection: A Replication Study
Author(s) -
Spindler Martin
Publication year - 2016
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.2432
Subject(s) - lasso (programming language) , replication (statistics) , instrumental variable , replicate , econometrics , selection (genetic algorithm) , variable (mathematics) , computer science , feature selection , core (optical fiber) , model selection , economics , statistics , machine learning , mathematics , telecommunications , mathematical analysis , world wide web
Summary Recently, Lasso methods have been applied to economic questions. In a seminal paper, Belloni et al . ( Econometrica ; 80 (6): 2369–2429) make use of (post‐)Lasso for instrumental variable selection in a setting where the number of instruments p is large or might even exceed the number of observations n —a situation which is prevalent in many current applications. We replicate their simulation study with the statistical package R (R Development Core Team ([, 2008])) and, moreover, analyze in more detail the importance of the choice of the penalization parameter, a crucial component in applications. Copyright © 2015 John Wiley & Sons, Ltd.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom