Classification Trees for Heterogeneous Moment-Based Models

Sam Asher, Denis Nekipelov, Paul Novosad, Stephen P. Ryan

NBER Working Paper No. 22976
Issued in December 2016
NBER Program(s):Development Economics, Industrial Organization, Labor Studies

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.

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Document Object Identifier (DOI): 10.3386/w22976

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