Scandinavian Working Papers in Economics

Discussion Papers,
Norwegian School of Economics, Department of Business and Management Science

No 2023/1: A two sample size estimator for large data sets

Martin O’Connell (), Howard Smith () and Øyvind 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
Howard Smith: Dept. of Economics, Oxford University, Postal: Oxford University , Department of Economics, Manor Road Building, Manor Road, Oxford, OX1 3UQ, United Kingdom
Øyvind Thomassen: Dept. of Business and Management Science, Norwegian School of Economics, Postal: NHH , Department of Business and Management Science, Helleveien 30, N-5045 Bergen, Norway

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

JEL-codes: C20; C51; C55

Language: English

25 pages, February 17, 2023

Full text files 

3051932 PDF-file Full text

Download statistics

Questions (including download problems) about the papers in this series should be directed to Stein Fossen ()
Report other problems with accessing this service to Sune Karlsson ().

RePEc:hhs:nhhfms:2023_001This page generated on 2024-09-13 22:16:23.