Artigo Acesso aberto Revisado por pares

Testing for parameter stability in nonlinear autoregressive models

2012; Wiley; Volume: 33; Issue: 3 Linguagem: Inglês

10.1111/j.1467-9892.2011.00764.x

ISSN

1467-9892

Autores

Claudia Kirch, Joseph Tadjuidje Kamgaing,

Tópico(s)

Fault Detection and Control Systems

Resumo

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.

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