You can manage bookmarks using lists, please log in to your user account for this.
Media type:
E-Article
Title:
Testing for parameter stability in nonlinear autoregressive models
Contributor:
Kirch, Claudia;
Kamgaing, Joseph Tadjuidje
Published:
Wiley, 2012
Published in:
Journal of Time Series Analysis, 33 (2012) 3, Seite 365-385
Language:
English
DOI:
10.1111/j.1467-9892.2011.00764.x
ISSN:
0143-9782;
1467-9892
Origination:
Footnote:
Description:
In this article we develop testing procedures for the detection of structural changes in nonlinear autoregressive processes. For the detection procedure, we model the regression function by a single layer feedforward neural network. We show that CUSUM‐type tests based on cumulative sums of estimated residuals, that have been intensively studied for linear regression, can be extended to this case. The limit distribution under the null hypothesis is obtained, which is needed to construct asymptotic tests. For a large class of alternatives, it is shown that the tests have asymptotic power one. In this case, we obtain a consistent change‐point estimator which is related to the test statistics. Power and size are further investigated in a small simulation study with a particular emphasis on situations where the model is misspecified, i.e. the data is not generated by a neural network but some other regression function. As illustration, an application on the Nile data set as well as S&P log‐returns is given.