Estimation in threshold autoregressive models with correlated innovations

P. Chigansky*, Yu A. Kutoyants

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Large sample statistical analysis of threshold autoregressive models is usually based on the assumption that the underlying driving noise is uncorrelated. In this paper, we consider a model, driven by Gaussian noise with geometric correlation tail and derive a complete characterization of the asymptotic distribution for the Bayes estimator of the threshold parameter.

Original languageAmerican English
Pages (from-to)959-992
Number of pages34
JournalAnnals of the Institute of Statistical Mathematics
Issue number5
StatePublished - Oct 2013

Bibliographical note

Funding Information:
P. Chigansky is supported by ISF grant 314/09.


  • Asymptotic statistics
  • Bayes estimator
  • Hidden Markov models
  • Threshold autoregression


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