%0 Journal Article
%@ 0883-7252
%A Advani, A
%A Kitagawa, T
%A Sloczynski, T
%D 2019
%F discovery:10072289
%I John Wiley and Sons
%J Journal of Applied Econometrics
%K empirical Monte Carlo studies, program evaluation, selection on observables,  treatment effects
%N 6
%P 893-910
%T Mostly Harmless Simulations? Using Monte Carlo Studies for Estimator Selection
%U https://discovery.ucl.ac.uk/id/eprint/10072289/
%V 34
%X We consider two recent suggestions for how to perform an empirically motivated Monte Carlo study to help select a treatment effect estimator under unconfoundedness. We show theoretically that neither is likely to be informative except under restrictive conditions that are unlikely to be satisfied in many contexts. To test empirical relevance, we also apply the approaches to a real-world setting where estimator performance is known. Both approaches are worse than random at selecting estimators which minimise absolute bias.They are better when selecting estimators that minimise mean squared error.However, using a simple bootstrap is at least as good and often better. For now researchers would be best advised to use a range of estimators and compare estimates for robustness.
%Z © 2020 The Authors. Journal of Applied Econometrics published by John Wiley & Sons Ltd.   This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.