A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Shape-Constrained Kernel-Weighted Least Squares: Estimating Production Functions for Chilean Manufacturing Industries
Tekijät: Yagi D, Chen YN, Johnson AL, Kuosmanen T
Kustantaja: AMER STATISTICAL ASSOC
Julkaisuvuosi: 2020
Journal: Journal of Business and Economic Statistics
Tietokannassa oleva lehden nimi: JOURNAL OF BUSINESS & ECONOMIC STATISTICS
Lehden akronyymi: J BUS ECON STAT
Vuosikerta: 38
Aloitussivu: 43
Lopetussivu: 54
Sivujen määrä: 12
ISSN: 0735-0015
DOI: https://doi.org/10.1080/07350015.2018.1431128
Tiivistelmä
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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