A two sample size estimator for large data sets

Martin O’Connell (), Howard Smith and Oyvind Thomassen
Additional contact information
Martin O’Connell: Dept. of Economics, University of Wisconsin-Madison, Postal: University of Wisconsin-Madison, Department of Economics, William H. Sewell Social Science Building, 1180 Observatory Drive, Madison, WI 53706-1393, USA, https://sites.google.com/site/drmartinoconnell/
Authors registered in the RePEc Author Service: Martin O'Connell

No 2023/1, Discussion Papers from Norwegian School of Economics, Department of Business and Management Science

Abstract: 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 an estimator that uses the full data set for the computationally cheap observed component, but a reduced sample size for the predicted component. We show consistency, asymptotic normality, and derive standard errors and a practical criterion for when our estimator is variance-reducing. We demonstrate the estimator’s properties on a range of models through Monte Carlo studies and an empirical application to alcohol demand.

Keywords: GMM; estimation; micro data (search for similar items in EconPapers)
JEL-codes: C20 C51 C55 (search for similar items in EconPapers)
Pages: 25 pages
Date: 2023-02-17
New Economics Papers: this item is included in nep-ecm and nep-ets
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https://hdl.handle.net/11250/3051932 Full text (application/pdf)

Related works:
Working Paper: A two sample size estimator for large data sets (2023)
Working Paper: A two sample size estimator for large data sets (2023)
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