Heteroscedasticity and/or autocorrelation diagnostics in nonlinear models with AR(1) and symmetrical errors

Chun Zheng Cao, Jin Guan Lin*, Lixing ZHU

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)

Abstract

In this paper, we discuss tests of heteroscedasticity and/or autocorrelation in nonlinear models with AR(1) and symmetrical errors. The symmetrical errors distribution class includes all symmetrical continuous distributions, such as normal, Student-t, power exponential, logistic I and II, contaminated normal, so on. First, score test statistics and their adjustment forms of heteroscedasticity are derived. Then, the asymptotic properties, including asymptotic chi-square and approximate powers under local alternatives of the score tests, are studied. The properties of test statistics are investigated through Monte Carlo simulations. Finally, a real data set is used to illustrate our test methods.

Original languageEnglish
Pages (from-to)813-836
Number of pages24
JournalStatistical Papers
Volume51
Issue number4
DOIs
Publication statusPublished - 2010

Scopus Subject Areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

User-Defined Keywords

  • Approximate local powers
  • AR(1) errors
  • Asymptotic properties
  • Heteroscedasticity
  • Nonlinear model
  • Score test
  • Symmetrical distributions

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