Bootstrapping the portmanteau tests in weak auto-regressive moving average models

• 成果类型: Article
• 署名作者: Zhu, Ke
• 署名单位: Chinese Academy of Sciences
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12112
• 发表日期: 2016
• 页码: 463-485
• 关键词: time-series models; least absolute deviation; Goodness-of-fit; generalized spectral tests; CONDITIONAL HETEROSCEDASTICITY; diagnostic checking; unknown form; residual autocorrelations; martingale hypothesis; COVARIANCE-MATRIX

摘要

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.