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

Fingerprint

Regime-switching Model
ARMA Model
Bayesian inference
Interest Rates
interest rate
Absorbing
regime
Structural Breaks
Metropolis-Hastings Algorithm
Regime Switching
Unit Root
Volatiles
Moving Average
Econometrics
Posterior distribution
Persistence
Efficient Algorithms
econometrics
persistence
Term

Keywords

  • Global Metropolis-Hastings algorithm; Proposal distribution

ASJC Scopus subject areas

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

Cite this

@article{4c6ea3e690594ae38d5a72647e3e1e27,
title = "Bayesian Inference in Regime-Switching ARMA Models With Absorbing States: The Dynamics of the Ex-Ante Real Interest Rate Under Regime Shifts",
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.",
keywords = "Global Metropolis-Hastings algorithm; Proposal distribution",
author = "Chang-Jin Kim and Jaeho Kim",
year = "2015",
month = "10",
day = "2",
doi = "10.1080/07350015.2014.979995",
language = "English",
volume = "33",
pages = "566--578",
journal = "Journal of Business and Economic Statistics",
issn = "0735-0015",
publisher = "American Statistical Association",
number = "4",

}

TY - JOUR

T1 - Bayesian Inference in Regime-Switching ARMA Models With Absorbing States

T2 - The Dynamics of the Ex-Ante Real Interest Rate Under Regime Shifts

AU - Kim, Chang-Jin

AU - Kim, Jaeho

PY - 2015/10/2

Y1 - 2015/10/2

N2 - 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.

AB - 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.

KW - Global Metropolis-Hastings algorithm; Proposal distribution

UR - http://www.scopus.com/inward/record.url?scp=84945313760&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84945313760&partnerID=8YFLogxK

U2 - 10.1080/07350015.2014.979995

DO - 10.1080/07350015.2014.979995

M3 - Article

VL - 33

SP - 566

EP - 578

JO - Journal of Business and Economic Statistics

JF - Journal of Business and Economic Statistics

SN - 0735-0015

IS - 4

ER -