A1 Refereed original research article in a scientific journal
Shape-Constrained Kernel-Weighted Least Squares: Estimating Production Functions for Chilean Manufacturing Industries
Authors: Yagi D, Chen YN, Johnson AL, Kuosmanen T
Publisher: AMER STATISTICAL ASSOC
Publication year: 2020
Journal: Journal of Business and Economic Statistics
Journal name in source: JOURNAL OF BUSINESS & ECONOMIC STATISTICS
Journal acronym: J BUS ECON STAT
Volume: 38
First page : 43
Last page: 54
Number of pages: 12
ISSN: 0735-0015
DOI: https://doi.org/10.1080/07350015.2018.1431128
Abstract
In this article, 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.
In this article, 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.
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