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Classification Trees for Heterogeneous Moment-Based Models

Sam AsherDenis NekipelovPaul NovosadStephen P. Ryan

出版	National Bureau of Economic Research, 2016
URL	http://books.google.com.hk/books?id=yWNLAQAACAAJ&hl=&source=gbs_api

註釋

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.