A two-sample size estimator for large datasets

Martin O’Connell

doi:10.1093/ectj/utaf002

A two-sample size estimator for large datasets

2025-01-09

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DOI：10.1093/ectj/utaf002

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In 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

A two-sample size estimator for large datasets

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