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

Daisuke Yagi*, Yining Chen, Andrew L. Johnson, Timo Kuosmanen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)

Abstract

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
Volume38
Issue number1
Early online date11 Jul 2018
DOIs
Publication statusPublished - 2 Jan 2020
MoE publication typeA1 Journal article-refereed

Fingerprint Dive into the research topics of 'Shape-Constrained Kernel-Weighted Least Squares: Estimating Production Functions for Chilean Manufacturing Industries'. Together they form a unique fingerprint.

  • Cite this