Semiparametric Regression with Kernel Error Model

Ao Yuan and Jan G. Gooijer

Scandinavian Journal of Statistics, 2007, vol. 34, issue 4, 841-869

Abstract: Abstract. We propose and study a class of regression models, in which the mean function is specified parametrically as in the existing regression methods, but the residual distribution is modelled non‐parametrically by a kernel estimator, without imposing any assumption on its distribution. This specification is different from the existing semiparametric regression models. The asymptotic properties of such likelihood and the maximum likelihood estimate (MLE) under this semiparametric model are studied. We show that under some regularity conditions, the MLE under this model is consistent (when compared with the possibly pseudo‐consistency of the parameter estimation under the existing parametric regression model), is asymptotically normal with rate and efficient. The non‐parametric pseudo‐likelihood ratio has the Wilks property as the true likelihood ratio does. Simulated examples are presented to evaluate the accuracy of the proposed semiparametric MLE method.

Date: 2007
References: View complete reference list from CitEc
Citations: View citations in EconPapers (17)

Downloads: (external link)
https://doi.org/10.1111/j.1467-9469.2006.00531.x

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: https://EconPapers.repec.org/RePEc:bla:scjsta:v:34:y:2007:i:4:p:841-869

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0303-6898

Access Statistics for this article

Scandinavian Journal of Statistics is currently edited by ÿrnulf Borgan and Bo Lindqvist

More articles in Scandinavian Journal of Statistics from Danish Society for Theoretical Statistics, Finnish Statistical Society, Norwegian Statistical Association, Swedish Statistical Association
Bibliographic data for series maintained by Wiley Content Delivery ().