Bayesian Inference in Regime-Switching ARMA Models With Absorbing States: The Dynamics of the Ex-Ante Real Interest Rate Under Regime Shifts
开发了一种高效的状态转换ARMA模型估计算法，并用于分析事前实际利率在制度转换下的动态，发现其可能比文献中记录的更持久和波动。
One goal of this article is to develop an efficient Metropolis-Hastings (MH) algorithm for estimating an ARMA model with a regime-switching mean, by designing a new efficient proposal distribution for the regime-indicator variable. Unlike the existing algorithm, our algorithm can achieve reasonably fast convergence to the posterior distribution even when the latent regime-indicator variable is highly persistent or when there exist absorbing states. Another goal is to appropriately investigate the dynamics of the latent ex-ante real interest rate (EARR) in the presence of structural breaks, by employing the econometric tool developed. We show that excluding the theory-implied moving-average terms may understate the persistence of the observed EPRR dynamics. Our empirical results suggest that, even though we rule out the possibility of a unit root in the EARR, it may be more persistent and volatile than has been documented in some of the literature.