Bootstrapping the portmanteau tests in weak auto-regressive moving average models
Journal of the Royal Statistical Society: Series B (Statistical Methodology) · 2016 · Volume 78 · Issue 2
Abstract
The paper uses a random-weighting (RW) method to bootstrap the critical values for the Ljung–Box or Monti portmanteau tests and weighted Ljung–Box or Monti portmanteau tests in weak auto-regressive moving average models. Unlike the existing methods, no user-chosen parameter is needed to implement the RW method. As an application, these four tests are used to check the model adequacy in power generalized auto-regressive conditional heteroscedasticity models. Simulation evidence indicates that the weighted portmanteau tests have a power advantage over other existing tests. A real example on the Standard and Poor's 500 index illustrates the merits of our testing procedure. As an extension, the blockwise RW method is also studied.
Keywords
Bootstrap method, Portmanteau test, Power generalized auto-regressive conditional heteroscedasticity models, Random-weighting approach, Weak auto-regressive moving average models, Weighted portmanteau test
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