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A Bayesian structural-change analysis via the stochastic approximation Monte Carlo and Gibbs sampler

Authors
Cheon, SooyoungKim, Jaehee
Issue Date
3-7월-2014
Publisher
TAYLOR & FRANCIS LTD
Keywords
stochastic approximation Monte Carlo; structural-change model; multiple changes; local trap
Citation
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, v.84, no.7, pp.1444 - 1470
Indexed
SCIE
SCOPUS
Journal Title
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
Volume
84
Number
7
Start Page
1444
End Page
1470
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/97979
DOI
10.1080/00949655.2012.747525
ISSN
0094-9655
Abstract
In this article, we propose a Bayesian approach to estimate the multiple structural change-points in a level and the trend when the number of change-points is unknown. Our formulation of the structural-change model involves a binary discrete variable that indicates the structural change. The determination of the number and the form of structural changes are considered as a model selection issue in Bayesian structural-change analysis. We apply an advanced Monte Carlo algorithm, the stochastic approximation Monte Carlo (SAMC) algorithm, to this structural-change model selection issue. SAMC effectively functions for the complex structural-change model estimation, since it prevents entrapment in local posterior mode. The estimation of the model parameters in each regime is made using the Gibbs sampler after each change-point is detected. The performance of our proposed method has been investigated on simulated and real data sets, a long time series of US real gross domestic product, US uses of force between 1870 and 1994 and 1-year time series of temperature in Seoul, South Korea.
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