Asymptotic Distribution-Free Diagnostic Tests For Heteroskedastic Time Series Models
This article investigates model checks for a class of possibly nonlinear heteroskedastic time series models, including but not restricted to ARMA-GARCH models. We propose omnibus tests based on functionals of certain weighted standardized residual empirical processes. The new tests are asymptotically distribution-free, suitable when the conditioning set is in?nite- dimensional, and consistent against a class of Pitman?s local alternatives converging at the parametric rate n??1=2; with n the sample size. A Monte Carlo study shows that the simulated level of the proposed tests is close to the asymptotic level already for moderate sample sizes and that tests have a satisfactory power performance. Finally, we illustrate our methodology with an application to the well-known S&P 500 daily stock index. The paper also contains an asymptotic uniform expansion for weighted residual empirical processes when initial conditions are considered, a result of independent interest.
|Date of creation:||Sep 2009|
|Contact details of provider:|| Postal: 812-855-1021|
Web page: http://www.iub.edu/~caepr
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:inu:caeprp:2009-019. 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: (Center for Applied Economics and Policy Research)
If references are entirely missing, you can add them using this form.