Mostly Harmless Simulations? Using Monte Carlo Studies for Estimator Selection

Arun Advani, Toru Kitagawa and Tymon Słoczyński
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Toru Kitagawa: University College London and cemmap

CAGE Online Working Paper Series from Competitive Advantage in the Global Economy (CAGE)

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.

Keywords: empirical Monte Carlo studies; program evaluation; selection on observables; treatment effects JEL Classification: C15; C21; C25; C52 (search for similar items in EconPapers)
Date: 2019
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (9)

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https://warwick.ac.uk/fac/soc/economics/research/c ... /411-2019_advani.pdf

Related works:
Journal Article: Mostly harmless simulations? Using Monte Carlo studies for estimator selection (2019)
Working Paper: Mostly Harmless Simulations? Using Monte Carlo Studies for Estimator Selection (2019)
Working Paper: Mostly Harmless Simulations? Using Monte Carlo Studies for Estimator Selection (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:cge:wacage:411

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