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

Research output: Contribution to journalArticle


Research units

  • Texas A and M University
  • The London School of Economics and Political Science
  • Osaka University


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.


Original languageEnglish
Pages (from-to)43-54
Number of pages12
JournalJournal of Business and Economic Statistics
Issue number1
Early online date11 Jul 2018
Publication statusPublished - 2 Jan 2020
MoE publication typeA1 Journal article-refereed

ID: 26844742