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.

中文翻译：

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机器学习的见解，可用于根据制造业调查数据评估生产函数估计量

在没有进行全面普查的年份中，国家统计组织经常依靠非详尽的调查来估计行业水平的生产功能。在分析非人口普查年份的数据时，我们建议根据其样本内加权和预测性能选择一个估计量。我们将Cobb–Douglas函数假设与现有的非参数形状约束估计器和新提出的估计器进行了比较。对于模拟数据，我们发现我们提出的估计器具有最低的加权误差。Cobb–Douglas规范使用2010年智利年度全国工业调查，描述了在最佳情况下至少有90％的变量是最佳替代估计量，而实际上，所有情况都被认为提供了两种见解：使用应用数据选择估计量的好处；以及噪声数据中结构的好处。最后，对于五个最大的制造业，我们发现平均而言，有30％的样本达到了R平方值的60％，而这是全盘普查所能达到的；但是，不同行业和样本之间的差异很大。