Estimation in threshold autoregressive models with correlated innovations

P. Chigansky; Yu A. Kutoyants

doi:10.1007/s10463-013-0402-4

Estimation in threshold autoregressive models with correlated innovations

P. Chigansky^*, Yu A. Kutoyants

^*Corresponding author for this work

Department of Statistics

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

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 language	English
Pages (from-to)	959-992
Number of pages	34
Journal	Annals of the Institute of Statistical Mathematics
Volume	65
Issue number	5
DOIs	https://doi.org/10.1007/s10463-013-0402-4
State	Published - Oct 2013

Bibliographical note

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

Keywords

Asymptotic statistics
Bayes estimator
Hidden Markov models
Threshold autoregression

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10.1007/s10463-013-0402-4

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abstract = "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.",

keywords = "Asymptotic statistics, Bayes estimator, Hidden Markov models, Threshold autoregression",

author = "P. Chigansky and Kutoyants, {Yu A.}",

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year = "2013",

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KW - Bayes estimator

KW - Hidden Markov models

KW - Threshold autoregression

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Estimation in threshold autoregressive models with correlated innovations

Abstract

Bibliographical note

Keywords

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