Diagnostic checking for time series models with conditional heteroscedasticity estimated by the least absolute deviation approach

Guodong LI, Wai Keung LI

Research output: Contribution to journalArticles

24 Citations (Scopus)

Abstract

The recent paper by Peng & Yao (2003) gave an interesting extension of least absolute deviation estimation to generalised autoregressive conditional heteroscedasticity, GARCH, time series models. The asymptotic distributions of absolute residual autocorrelations and squared residual autocorrelations from the GARCH model estimated by the least absolute deviation method are derived in this paper. These results lead to two useful diagnostic tools which can be used to check whether or not a GARCH model fitted by using the least absolute deviation method is adequate. Some simulation experiments give further support to the asymptotic theory and a real data example is also reported. Copyright © 2005 Biometrika Trust.
Original languageEnglish
Pages (from-to)691-701
JournalBiometrika
Volume92
Issue number3
DOIs
Publication statusPublished - Sep 2005

Citation

Li, G., & Li, W. K. (2005). Diagnostic checking for time series models with conditional heteroscedasticity estimated by the least absolute deviation approach. Biometrika, 92(3), 691-701. doi: 10.1093/biomet/92.3.691

Keywords

  • Absolute residual autocorrelation
  • Asymptotic distribution
  • Diagnostic checking
  • GARCH model
  • Local least absolute deviation estimator
  • Squared residual autocorrelation

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