Advani, A;
Kitagawa, T;
Sloczynski, T;
(2019)
Mostly Harmless Simulations? Using Monte Carlo Studies for Estimator Selection.
Journal of Applied Econometrics
, 34
(6)
pp. 893-910.
10.1002/jae.2724.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | Mostly Harmless Simulations? Using Monte Carlo Studies for Estimator Selection |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1002/jae.2724 |
| Publisher version: | https://doi.org/10.1002/jae.2724 |
| Language: | English |
| Additional information: | © 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. |
| Keywords: | empirical Monte Carlo studies, program evaluation, selection on observables, treatment effects |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL SLASH UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Economics |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10072289 |
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