Bayesian Regime-switching Analysis via Stochastic Approximation Monte Carlo

• 발행기관: 한국자료분석학회
• 저자명: Sooyoung Cheon Hee-chan Lee
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.13 No.2, 599~609쪽, 전체 11쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2011.04.30

영문 초록

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.

목차

1. Introduction

2. Stochastic approximation Monte Carlo

3. Bayesian Regime-switching Model Selection Problems

4. Numerical results

5. Conclusion

Reference

키워드

Stochastic approximation Monte Carlo local trap Bayesian regimeswitching model monthly simple returns quarterly unemployment rate