Insights from machine learning for evaluating production function estimators on manufacturing survey data

José Luis Preciado Arreola, Daisuke Yagi and Andrew Johnson
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José Luis Preciado Arreola: Texas A&M University
Daisuke Yagi: Texas A&M University

Journal of Productivity Analysis, 2020, vol. 53, issue 2, No 3, 225 pages

Abstract: Abstract National statistical organizations often 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 assumption 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. Using 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. Finally for the five largest manufacturing industries, we find that a 30% sample, on average, achieves 60% of the R-squared value that would have been achieved with a full census; however, the variance across industries and samples is large.

Keywords: Convex nonparametric least squares; Adaptive partitioning; Multivariate convex regression; Nonparametric stochastic frontier; C30; C61 (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s11123-019-00570-9

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