A two sample size estimator for large data sets
In 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 size “lar...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Oxford University Press
2025
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| Summary: | In 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 size “large-small” estimator that uses the full data set 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 the asymptotically efficient CEP-GMM estimator of Chen et al. (2005) and Chen et al. (2008) 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.
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