Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/204699 
Autor:innen: 
Erscheinungsjahr: 
2019
Schriftenreihe/Nr.: 
Discussion Papers of the Max Planck Institute for Research on Collective Goods No. 2019/1
Verlag: 
Max Planck Institute for Research on Collective Goods, Bonn
Zusammenfassung: 
Frequently in experiments there is not only variance in the reaction of participants to treatment. The heterogeneity is patterned: discernible types of participants react differently. In principle, a finite mixture model is well suited to simultaneously estimate the probability that a given participant belongs to a certain type, and the reaction of this type to treatment. Yet often, finite mixture models need more data than the experiment provides. The approach requires ex ante knowledge about the number of types. Finite mixture models are hard to estimate for panel data, which is what experiments often generate. For repeated experiments, this paper offers a simple two-step alternative that is much less data hungry, that allows to find the number of types in the data, and that allows for the estimation of panel data models. It combines machine learning methods with classic frequentist statistics.
Schlagwörter: 
heterogeneous treatment effect
finite mixture model
panel data
two-step approach
machine learning
CART
JEL: 
C14
C23
C91
Dokumentart: 
Working Paper

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