A two-sample size estimator for large datasets

Martin O’Connell, Howard Smith and Øyvind Thomassen

The Econometrics Journal, 2025, vol. 28, issue 3, 406-422

Abstract: SummaryIn generalized method of moments (GMM) estimators, moment conditions with additive error terms involve an observed component and a predicted component. If the predicted component is computationally costly to evaluate, it may not be feasible to estimate the model with all the available data. We propose a simple two-sample ‘large–small’ size estimator that uses the full dataset for the computationally cheap observed component, but a reduced sample size for the predicted component. We derive a practical criterion for when the large-small estimator has a lower variance than standard GMM with the reduced sample size. As an alternative, we show how a previously described asymptotically efficient conditional expectation projection based GMM estimator can also be used to reduce computational cost in our setting. We compare the performance of the estimators in a Monte Carlo study of a panel-data random coefficients logit model, and illustrate the use of our estimator in an empirical application to alcohol demand.

Keywords: GMM; sample combination; estimation; micro data (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:oup:emjrnl:v:28:y:2025:i:3:p:406-422.

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