Bayesian Regime-switching Analysis via Stochastic Approximation Monte CarloBayesian Regime-switching Analysis via Stochastic Approximation Monte Carlo
- Other Titles
- Bayesian Regime-switching Analysis via Stochastic Approximation Monte Carlo
- Authors
- 전수영; 이희찬
- Issue Date
- 2011
- Publisher
- 한국자료분석학회
- Keywords
- Stochastic approximation Monte Carlo; local trap; Bayesian regime- switching model; monthly simple returns; quarterly unemployment rate.
- Citation
- Journal of The Korean Data Analysis Society, v.13, no.2, pp.599 - 609
- Indexed
- KCI
- Journal Title
- Journal of The Korean Data Analysis Society
- Volume
- 13
- Number
- 2
- Start Page
- 599
- End Page
- 609
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/113456
- ISSN
- 1229-2354
- Abstract
- Monte Carlo methods have received much attention in the recent literature of the regime- switching analysis. However, the conventional Markov chain Monte Carlo (MCMC) algorithms, such as Metropolis-Hastings, tend to get trapped in a local mode in simulating from the posterior distribution of regime-switching time-series, rendering the inference ineffective. In this paper, we focus on the finding the best likelihood value in Bayesian nonlinear time-series model (Kim and Cheon, 2010) and the detection of multiple regime-switching in monthly simple returns and quarterly unemployment rate via the stochastic approximation Monte Carlo algorithm (Liang et al., 2007). The numerical results indicate that our method outperforms MCMC significantly for the regime-switching identification, and provide 3 and 5 regime switchings in monthly simple returns and quarterly unemployment rate, respectively.
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Collections - Graduate School > Department of Applied Statistics > 1. Journal Articles
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