U-statistic Type Tests for Structural Breaks in Linear Regression Models

Jose Olmo and William Pouliot

No 08/15, City University Economics Discussion Papers from Department of Economics, City University, London

Abstract: This article introduces a U-statistic type process that is based on a kernel function which can depend on nuisance parameters. It is shown here that this process can accommodate very easily anti-symmetric kernels very useful for detecting changing patterns in the dynamics of time series. This theory is applied to structural break hypothesis tests in linear regression models. In particular, the flexibility of these processes will be exploited to introduce a simultaneous and joint test that exhibit statistical power against changes in either intercept or slope. In contrast to the literature, these tests are able to distinguish between rejections due to changes in intercept from rejections due to changes in slope; allow control of global errors rate; and are explicitly designed to have power when the distribution error is asymmetric. These tests can also incorporate different weight functions devised to detect changes early as well as later on in the sample, and show very good performance in small samples. These tests, therefore, outperform CUSUM type tests widely employed in this literature.

Keywords: Change-Point tests; CUSUM test; Linear regression models; Stochastic processes; U-statistics (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm
Date: 2008-11
View list of references

Downloads: (external link)
http://www.city.ac.uk/economics/dps/discussion_papers/0815.pdf (application/pdf)

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: http://EconPapers.repec.org/RePEc:cty:dpaper:0815

Access Statistics for this paper

More papers in City University Economics Discussion Papers from Department of Economics, City University, London
Contact information at EDIRC.
Series data maintained by Michael Ben-Gad ().