A TEST FOR CONDITIONAL HETEROSKEDASTICITY IN TIME SERIES MODELS

A. K. Bera, M. L. Higgins

Research output: Contribution to journalArticlepeer-review

Abstract

Abstract. When testing for conditional heteroskedasticity and nonlinearity, the power of the test in general depends on the functional forms of conditional heteroskedasticity and nonlinearity that are allowed under the alternative hypothesis. We suggest a test for conditional heteroskedasticity and nonlinearity with the nonlinear autoregressive conditional heteroskedasticity model of Higgins and Bera as the alternative. Standard testing procedures are not applicable since our nonlinear autoregressive conditional heteroskedasticity (ARCH) parameter is not identified under the null hypothesis. To resolve this problem, we apply the procedure recently proposed by Davies. Power and size of the suggested test are investigated through simulation, and an empirical application of testing for ARCH in exchange rates is also discussed.

Original languageEnglish (US)
Pages (from-to)501-519
Number of pages19
JournalJournal of Time Series Analysis
Volume13
Issue number6
DOIs
StatePublished - Nov 1992

Keywords

  • ARCH
  • Davies' test
  • NARCH
  • bilinear
  • nonlinear time series models
  • nonlinearity

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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