Bayesian Inference in Regime-Switching ARMA Models With Absorbing States: The Dynamics of the Ex-Ante Real Interest Rate Under Regime Shifts

Chang-Jin Kim, Jaeho Kim

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)566-578
Number of pages13
JournalJournal of Business and Economic Statistics
Volume33
Issue number4
DOIs
Publication statusPublished - 2015 Oct 2
Externally publishedYes

Keywords

  • Global Metropolis-Hastings algorithm; Proposal distribution

ASJC Scopus subject areas

  • Statistics and Probability
  • Economics and Econometrics
  • Social Sciences (miscellaneous)
  • Statistics, Probability and Uncertainty

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