Insights from Machine Learning for Evaluating Production Function Estimators on Manufacturing Survey Data
Abstract
Organizations like U.S. Census Bureau rely on non-exhaustive surveys to estimate industry-level production functions in years in which a full Census is not conducted. When analyzing data from non-census years, we propose selecting an estimator based on a weighting of its in-sample and predictive performance. We compare Cobb-Douglas functional assumptions to existing nonparametric shape constrained estimators and a newly proposed estimator. 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 providing two insights: the benefits of using application data for selecting an estimator, and the benefits of structure in noisy data.
Cite
@article{arxiv.1604.04687,
title = {Insights from Machine Learning for Evaluating Production Function Estimators on Manufacturing Survey Data},
author = {José Luis Preciado Arreola and Daisuke Yagi and Andrew L. Johnson},
journal= {arXiv preprint arXiv:1604.04687},
year = {2018}
}