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Bootstrapping the portmanteau tests in weak auto‐regressive moving average models
Author(s) -
Zhu Ke
Publication year - 2016
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/rssb.12112
Subject(s) - bootstrapping (finance) , heteroscedasticity , weighting , moving average , autoregressive model , autoregressive–moving average model , autoregressive conditional heteroskedasticity , econometrics , mathematics , statistics , computer science , volatility (finance) , medicine , radiology
Summary 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.