U-statistic Type Tests for Structural Breaks in Linear Regression Models
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.
|Date of creation:||2008|
|Date of revision:|
|Contact details of provider:|| Postal: |
Phone: +44 (0)20 7040 8500
Web page: http://www.city.ac.uk
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Ploberger, Werner & Kramer, Walter & Kontrus, Karl, 1989. "A new test for structural stability in the linear regression model," Journal of Econometrics, Elsevier, vol. 40(2), pages 307-318, February.
- Kramer, Walter & Ploberger, Werner & Alt, Raimund, 1988. "Testing for Structural Change in Dynamic Models," Econometrica, Econometric Society, vol. 56(6), pages 1355-69, November.
- Filippo Altissimo & Valentina Corradi, 2000. "Strong Rules for Detecting the Number of Breaks in a Time Series," Econometric Society World Congress 2000 Contributed Papers 0574, Econometric Society.
- Altissimo, Filippo & Corradi, Valentina, 2003. "Strong rules for detecting the number of breaks in a time series," Journal of Econometrics, Elsevier, vol. 117(2), pages 207-244, December.
- Olmo, J. & Pouliot, W., 2008.
"Early Detection Techniques for Market Risk Failure,"
08/09, Department of Economics, City University London.
- Olmo Jose & Pouliot William, 2011. "Early Detection Techniques for Market Risk Failure," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(4), pages 1-55, September.
- Gombay Edit & Horváth Lajos & Husková Marie, 1996. "Estimators And Tests For Change In Variances," Statistics & Risk Modeling, De Gruyter, vol. 14(2), pages 145-160, February.
When requesting a correction, please mention this item's handle: RePEc:cty:dpaper:08/15. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Research Publications Librarian)
If references are entirely missing, you can add them using this form.