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arXiv · 1604.04687

Insights from Machine Learning for Evaluating Production Function Estimators on Manufacturing Survey Data

José Luis Preciado Arreola·Daisuke Yagi·Andrew L. Johnson
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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.

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José Luis Preciado Arreola, Daisuke Yagi, Andrew L. Johnson. 2016-04-16. Insights from Machine Learning for Evaluating Production Function Estimators on Manufacturing Survey Data. https://arxiv.org/abs/1604.04687

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