Diagnostic checking in a flexible nonlinear time series model

Marcelo C. Medeiros, Alvaro Veiga

Research output: Contribution to journalArticlepeer-review


This paper considers a sequence of misspecification tests for a flexible nonlinear time series model. The model is a generalization of both the smooth transition autoregressive (STAR) and the autoregressive artificial neural network (AR-ANN) models. The tests are Lagrange multiplier (LM) type tests of parameter constancy against the alternative of smoothly changing ones, of serial independence, and of constant variance of the error term against the hypothesis that the variance changes smoothly between regimes. The small sample behaviour of the proposed tests is evaluated by a Monte-Carlo study and the results show that the tests have size close to the nominal one and a good power.

Original languageEnglish (US)
Pages (from-to)461-482
Number of pages22
JournalJournal of Time Series Analysis
Issue number4
StatePublished - Jul 2003
Externally publishedYes


  • Heteroscedasticity, misspecification
  • Neural networks
  • Nonlinear models
  • Parameter constancy
  • Serial independence
  • Star models
  • Statistical inference
  • Time series

ASJC Scopus subject areas

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


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