Classification Trees for Heterogeneous Moment-Based Models / Sam Asher, Denis Nekipelov, Paul Novosad, Stephen P. Ryan.

Format:

Book

Author/Creator:

Asher, Sam.

Contributor:

National Bureau of Economic Research.

Nekipelov, Denis.

Novosad, Paul.

Ryan, Stephen P.

Series:

Working Paper Series (National Bureau of Economic Research) no. w22976.

NBER working paper series no. w22976

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2016.

Summary:

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.

Notes:

Print version record

December 2016.