A note on the corrected Akaike information criterion for threshold autoregressive models

C. S. WONG, Wai Keung LI

Research output: Contribution to journalArticles

32 Citations (Scopus)

Abstract

A bias‐corrected Akaike information criterion AICC is derived for self‐exciting threshold autoregressive (SETAR) models. The small sample properties of the Akaike information criteria (AIC, AICC) and the Bayesian information criterion (BIC) are studied using simulation experiments. It is suggested that AICC performs much better than AIC and BIC in small samples and should be put in routine usage. Copyright © 1998 Blackwell Publishers Ltd.
Original languageEnglish
Pages (from-to)113-124
JournalJournal of Time Series Analysis
Volume19
Issue number1
DOIs
Publication statusPublished - Jan 1998

Citation

Wong, C. S., & Li, W. K. (1998). A note on the corrected Akaike information criterion for threshold autoregressive models. Journal of Time Series Analysis, 19(1), 113-124. doi: 10.1111/1467-9892.00080

Keywords

  • Corrected Akaike information criterion
  • Kullback-Leibler information
  • Threshold time series model

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