Shape constrained kernel-weighted least squares: Estimating production functions for Chilean manufacturing industries

Daisuke Yagi, Yining Chen, Andrew Johnson and Timo Kuosmanen

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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. In addition, we propose a test to validate whether shape constraints are correctly specified. 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

Keywords: Local Polynomials; Kernel Estimation; Multivariate Convex Regression; Nonparametric regression; Shape Constraints (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Date: 2018-01-23
New Economics Papers: this item is included in nep-ecm and nep-eff
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Citations: View citations in EconPapers (4)

Published in Journal of Business and Economic Statistics, 23, January, 2018, pp. 0-0. ISSN: 0735-0015

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Related works:
Journal Article: Shape-Constrained Kernel-Weighted Least Squares: Estimating Production Functions for Chilean Manufacturing Industries (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:86556

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