Classification Trees for Heterogeneous Moment-Based Models
Denis Nekipelov (),
Paul Novosad and
Stephen Ryan ()
No 22976, NBER Working Papers from National Bureau of Economic Research, Inc
A basic problem in applied settings is that different parameters may apply to the same model in different populations. We address this problem by proposing a method using moment trees; leveraging the basic intuition of a classification tree, our method partitions the covariate space into disjoint subsets and fits a set of moments within each subspace. We prove the consistency of this estimator and show standard rates of convergence apply post-model selection. Monte Carlo evidence demonstrates the excellent small sample performance and faster-than-parametric convergence rates of the model selection step in two common empirical contexts. Finally, we showcase the usefulness of our approach by estimating heterogeneous treatment effects in a regression discontinuity design in a development setting.
JEL-codes: C14 C18 C51 C52 O12 O18 (search for similar items in EconPapers)
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