One for All and All for One: Regression Checks With Many Regressors
Pascal Lavergne and
Valentin Patilea
Journal of Business & Economic Statistics, 2011, vol. 30, issue 1, 41-52
Abstract:
We develop a novel approach to building checks of parametric regression models when many regressors are present, based on a class of sufficiently rich semiparametric alternatives, namely single-index models. We propose an omnibus test based on the kernel method that performs against a sequence of directional nonparametric alternatives as if there was only one regressor whatever the number of regressors. This test can be viewed as a smooth version of the integrated conditional moment test of Bierens. Qualitative information can be easily incorporated into the procedure to enhance power. In an extensive comparative simulation study, we find that our test is not very sensitive to the smoothing parameter and performs well in multidimensional settings. We apply this test to a cross-country growth regression model.
Date: 2011
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1198/jbes.2011.07152 (text/html)
Access to full text is restricted to subscribers.
Related works:
Working Paper: One for all and all for one: regression checks with many regressors (2011) 
Working Paper: One for All and All for One:Regression Checks With Many Regressors (2008) 
Working Paper: One for All and All for One: Regression Checks with Many Regressors" (2007) 
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:taf:jnlbes:v:30:y:2011:i:1:p:41-52
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UBES20
DOI: 10.1198/jbes.2011.07152
Access Statistics for this article
Journal of Business & Economic Statistics is currently edited by Eric Sampson, Rong Chen and Shakeeb Khan
More articles in Journal of Business & Economic Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().