Abstract: In this paper we propose selecting an estimator based on a weighting of its in-sample and predictive performance on actual application datasets. For simulated data, we find that our proposed estimator has the lowest weighted errors. For actual data, specifically the 2010 Chilean Annual National Industrial Survey, a Cobb-Douglas specification describes at least 90% as much variance as the best alternative estimators in practically all cases considered.
July 4 – Data Envelopment Analysis International Conference – Insights from Machine Learning for Evaluating Production Function Estimators on Manufacturing Survey Data
Posted in Seminars.