Shape constrained kernel-weighted least squares: Application to production function estimation for Chilean manufacturing industries
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.
Keywords
Cite
@article{arxiv.1604.06003,
title = {Shape constrained kernel-weighted least squares: Application to production function estimation for Chilean manufacturing industries},
author = {Daisuke Yagi and Yining Chen and Andrew L. Johnson and Timo Kuosmanen},
journal= {arXiv preprint arXiv:1604.06003},
year = {2018}
}
Comments
JEL Codes: C14, D24 Keywords: Kernel Estimation, Multivariate Convex Regression, Nonparametric regression, Shape Constraints