Two-Stage Least Squares Random Forests with an Application to Angrist and Evans (1998)

Martin Biewen and Philipp Kugler ()
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Philipp Kugler: Institut für Angewandte Wirtschaftsforschung (IAW)

No 13613, IZA Discussion Papers from Institute of Labor Economics (IZA)

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.

Keywords: fertility; generalized random forests; machine learning; instrumental variable estimation (search for similar items in EconPapers)
JEL-codes: C14 C26 C55 J13 J22 (search for similar items in EconPapers)
Pages: 24 pages
Date: 2020-08
New Economics Papers: this item is included in nep-big and nep-ecm
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Citations: View citations in EconPapers (1)

Published - shorter version published in: Economics Letters, 2021, 204, 109893

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Journal Article: Two-stage least squares random forests with an application to Angrist and Evans (1998) (2021)
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