Testing a linear time series model against its threshold extension

Guodong LI, Wai Keung LI

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

This paper derives the asymptotic null distribution of a quasilikelihood ratio test statistic for an autoregressive moving average model against its threshold extension. The null hypothesis is that of no threshold, and the error term could be dependent. The asymptotic distribution is rather complicated, and all existing methods for approximating a distribution in the related literature fail to work. Hence, a novel bootstrap approximation based on stochastic permutation is proposed in this paper. Besides being robust to the assumptions on the error term, our method enjoys more flexibility and needs less computation when compared with methods currently used in the literature. Monte Carlo experiments give further support to the new approach, and an illustration is reported. Copyright © 2011 Biometrika Trust.
Original languageEnglish
Pages (from-to)243-250
JournalBiometrika
Volume98
Issue number1
DOIs
Publication statusPublished - Mar 2011

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Time Series Models
Linear Time
Time series
time series analysis
Linear Model
Error term
Testing
Asymptotic distribution
Ratio test
Autoregressive Moving Average Model
Quasi-likelihood
Monte Carlo Experiment
Null Distribution
testing
Statistics
Null hypothesis
Bootstrap
Test Statistic
Permutation
statistics

Citation

Li, G., & Li, W. K. (2011). Testing a linear time series model against its threshold extension. Biometrika, 98(1), 243-250. doi: 10.1093/biomet/asq074

Keywords

  • Autoregressive moving average model
  • Bootstrap method
  • Quasilikelihood ratio test
  • Threshold model