TY - INPR
A1 - Kirch, Claudia
A1 - Tadjuidje Kamgaing, Joseph
T1 - Testing for parameter stability in nonlinear autoregressive models
N2 - In this paper 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.
T3 - Report in Wirtschaftsmathematik (WIMA Report) - 137
KW - Change analysis
KW - nonparametric regression
KW - neural network
KW - autoregressive process
Y1 - 2011
UR - https://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/2301
UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:386-kluedo-16914
ER -