Skip to main content

A new test for the parametric form of the variance function in non-parametric regression

Buy Article:

$51.00 plus tax (Refund Policy)



In the common non-parametric regression model the problem of testing for the parametric form of the conditional variance is considered. A stochastic process based on the difference between the empirical processes that are obtained from the standardized non-parametric residuals under the null hypothesis (of a specific parametric form of the variance function) and the alternative is introduced and its weak convergence established. This result is used for the construction of a Kolmogorov–Smirnov and a Cramér–von Mises type of statistic for testing the parametric form of the conditional variance. The consistency of a bootstrap approximation is established, and the finite sample properties of this approximation are investigated by means of a simulation study. In particular the new procedure is compared with some of the currently available methods for this problem.

Keywords: Bootstrap; Kernel estimation; Non-parametric regression; Residual distribution; Testing heteroscedasticity; Testing homoscedasticity

Document Type: Research Article


Affiliations: 1: Ruhr-Universität Bochum, Germany 2: Universität Hamburg, Germany 3: Université catholique de Louvain, Belgium

Publication date: 2007-11-01

  • Access Key
  • Free ContentFree content
  • Partial Free ContentPartial Free content
  • New ContentNew content
  • Open Access ContentOpen access content
  • Partial Open Access ContentPartial Open access content
  • Subscribed ContentSubscribed content
  • Partial Subscribed ContentPartial Subscribed content
  • Free Trial ContentFree trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more