Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1)

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




Julkaisun tekijät: Yagi D, Chen YN, Johnson AL, Kuosmanen T

Kustantaja: AMER STATISTICAL ASSOC

Julkaisuvuosi: 2020

Journal: Journal of Business and Economic Statistics

Tietokannassa oleva lehden nimi: JOURNAL OF BUSINESS & ECONOMIC STATISTICS

Lehden akronyymi: J BUS ECON STAT

Volyymi: 38

Sivujen määrä: 12

ISSN: 0735-0015

DOI: http://dx.doi.org/10.1080/07350015.2018.1431128


Tiivistelmä
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.


Last updated on 2022-04-10 at 19:29