Bayesian tests for unit root and multiple breaks

Man Suk Oh; Dong Wan Shin

doi:10.1080/02664760903173450

Bayesian tests for unit root and multiple breaks

Man Suk Oh, Dong Wan Shin

Department of Statistics

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

Abstract

A Bayesian approach is considered for identifying sources of nonstationarity for models with a unit root and breaks. Different types of multiple breaks are allowed through crash models, changing growth models, and mixed models. All possible nonstationary models are represented by combinations of zero or nonzero parameters associated with time trends, dummy for breaks, or previous levels, for which Bayesian posterior probabilities are computed. Multiple tests based on Markov chain Monte Carlo procedure sare implemented. The proposed method is applied to a real data set, the Korean GDP data set, showing a strong evidence for two breaks rather than the usual unit root or one break.

Original language	English
Pages (from-to)	1863-1874
Number of pages	12
Journal	Journal of Applied Statistics
Volume	37
Issue number	11
DOIs	https://doi.org/10.1080/02664760903173450
State	Published - Nov 2010

Bibliographical note

Funding Information:
The authors are grateful for the valuable comments of two referees. This work was supported by the Korea Research Foundation Grant funded by the Korean Government (MOEHRD, Basic Research Promotion Fund) (KRF-2008-C00017).

Keywords

Markov chain Monte Carlo
Multiple breaks
Unit root test

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10.1080/02664760903173450

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abstract = "A Bayesian approach is considered for identifying sources of nonstationarity for models with a unit root and breaks. Different types of multiple breaks are allowed through crash models, changing growth models, and mixed models. All possible nonstationary models are represented by combinations of zero or nonzero parameters associated with time trends, dummy for breaks, or previous levels, for which Bayesian posterior probabilities are computed. Multiple tests based on Markov chain Monte Carlo procedure sare implemented. The proposed method is applied to a real data set, the Korean GDP data set, showing a strong evidence for two breaks rather than the usual unit root or one break.",

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Bayesian tests for unit root and multiple breaks

Abstract

Bibliographical note

Keywords

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