Shape-constrained nonparametric production function estimator based on Regular Ultra Passum law and convex non-homothetic isoquants, applied to 1997-2007 Japanese corrugated cardboard data to analyze scale, input mix, and productivity.
Shape constrained kernel-weighted least squares: Application to production function estimation for Chilean manufacturing industries
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abstract
In this paper we examine a novel way of imposing shape constraints on a local polynomial kernel estimator. The proposed approach is referred to as Shape Constrained Kernel-weighted Least Squares (SCKLS). We prove uniform consistency of the SCKLS estimator with monotonicity and convexity/concavity constraints and establish its convergence rate. The competitiveness of SCKLS is shown in a comprehensive simulation study. Finally, we analyze Chilean manufacturing data using the SCKLS estimator and quantify production in the plastics and wood industries. The results show that exporting firms have significantly higher productivity.
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An axiomatic nonparametric production function estimator: Modeling production in Japan's cardboard industry
Shape-constrained nonparametric production function estimator based on Regular Ultra Passum law and convex non-homothetic isoquants, applied to 1997-2007 Japanese corrugated cardboard data to analyze scale, input mix, and productivity.