期刊
JOURNAL OF BUSINESS & ECONOMIC STATISTICS
卷 38, 期 1, 页码 43-54出版社
AMER STATISTICAL ASSOC
DOI: 10.1080/07350015.2018.1431128
关键词
Kernel estimation; Local polynomials; Multivariate convex regression; Nonparametric regression; Shape constraints
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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