关键词: 门限协整, 非线性, MCMC, 贝叶斯分析, ECM

Abstract: In the existing threshold cointegration methods, the jagged and potentially multimodal nature of the likelihood function of threshold model complicates optimization and also makes the identification of unknown nuisance parameters more difficult. This paper proposes nonlinear threshold ECM and conducts Bayesian inference. Based on the posterior conditional distributions of the parameters, MCMC samplers are designed. The finite sample property is studied via a combination of Monte Carlo simulation. Finally, through the empirical application of the US interest rates with various maturities, we find there are threshold cointegration relationships between interest rates with maturities of 1 month and 3 month, 3 month and 6 month, 3 month and 1 year, respectively. Therefore, the usefulness of this Bayesian method is demonstrated. It shows that Bayesian threshold cointegration solves the problem in complicated optimization and improves the precision of cointegration test.

Key words: Threshold Cointegration, Nonlinear, MCMC, Bayesian Analysis, ECM