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

Tutkimustuotos: Lehtiartikkeli

Tutkijat

Organisaatiot

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

Kuvaus

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.

Yksityiskohdat

AlkuperäiskieliEnglanti
Sivut43-54
Sivumäärä12
JulkaisuJournal of Business and Economic Statistics
Vuosikerta38
Numero1
Varhainen verkossa julkaisun päivämäärä11 heinäkuuta 2018
TilaJulkaistu - 2 tammikuuta 2020
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

ID: 26844742