Shape-Constrained Kernel-Weighted Least Squares: Estimating Production Functions for Chilean Manufacturing Industries uri icon

abstract

  • & #xa9; 2018 American Statistical Association 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.

published proceedings

  • Journal of Business & Economic Statistics

altmetric score

  • 0.5

author list (cited authors)

  • Yagi, D., Chen, Y., Johnson, A. L., & Kuosmanen, T.

citation count

  • 25

complete list of authors

  • Yagi, Daisuke||Chen, Yining||Johnson, Andrew L||Kuosmanen, Timo

publication date

  • January 2020