Goodness-of-fit Tests for Linear and Non-linear Time Series Models
In this article we study a general class of goodness-of-fit tests for the conditional mean of a linear or nonlinear time series model. Among the properties of the proposed tests are that they are suitable when the conditioning set is infinite-dimensional; are consistent against a broad class of alternatives including Pitman's local alternatives converging at the parametric rate; and do not need to choose a lag order depending on the sample size or to smooth the data. It turns out that the asymptotic null distributions of the tests depend on the data generating process, so a new bootstrap procedure is proposed and theoretically justified. The proposed bootstrap tests are robust to higher order dependence, in particular to conditional heteroskedasticity of unknown form. A simulation study compares the finite sample performance of the proposed and competing tests and shows that our tests can play a valuable role in time series modeling. Finally, an application to an economic price series highlights the merits of our approach.
|Date of creation:||01 Feb 2005|
|Date of revision:|
|Publication status:||Published, Journal of American Statistical Association, 2006, vol. 101(474): pp. 531-541|
|Contact details of provider:|| Web page: http://www.unav.edu/web/facultad-de-ciencias-economicas-y-empresariales|
References listed on IDEAS
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