Economics at your fingertips  

Semiparametric Smooth Coefficient Models

Li, Qi, et al
Authors registered in the RePEc Author Service: Dong Li () and Qi Li

Journal of Business & Economic Statistics, 2002, vol. 20, issue 3, 412-22

Abstract: In this article, we propose a semiparametric smooth coefficient model as a useful yet flexible specification for studying a general regression relationship with varying coefficients. The article proposes a local least squares method with a kernel weight function to estimate the smooth coefficient function. The consistency of the estimator and its asymptotic normality are established. A simple statistic for testing a parametric model versus the semiparametric smooth coefficient model is proposed. An empirical application of the proposed method is presented with an estimation of the production function of the nonmetal mineral industry in China. The empirical findings show that the intermediate production and management expense has played a vital role and is an unbalanced determinant of the labor and capital elasticities of output in production.

Date: 2002
References: Add references at CitEc
Citations: View citations in EconPapers (125) Track citations by RSS feed

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Ordering information: This journal article can be ordered from

Access Statistics for this article

Journal of Business & Economic Statistics is currently edited by Jonathan H. Wright and Keisuke Hirano

More articles in Journal of Business & Economic Statistics from American Statistical Association
Bibliographic data for series maintained by Christopher F. Baum ().

Page updated 2022-03-27
Handle: RePEc:bes:jnlbes:v:20:y:2002:i:3:p:412-22