Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/224055
Authors: 
Biewen, Martin
Kugler, Philipp
Year of Publication: 
2020
Series/Report no.: 
IZA Discussion Papers No. 13613
Abstract: 
We develop the case of two-stage least squares estimation (2SLS) in the general framework of Athey et al. (Generalized Random Forests, Annals of Statistics, Vol. 47, 2019) and provide a software implementation for R and C++. We use the method to revisit the classic application of instrumental variables in Angrist and Evans (Children and Their Parents' Labor Supply: Evidence from Exogenous Variation in Family Size, American Economic Review, Vol. 88, 1998). The two-stage least squares random forest allows one to investigate local heterogenous effects that cannot be investigated using ordinary 2SLS.
Subjects: 
machine learning
generalized random forests
fertility
instrumental variable estimation
JEL: 
C26
C55
J22
J13
C14
Document Type: 
Working Paper

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