TY - JOUR
T1 - Bayesian analysis of ARMA-GARCH models
T2 - A Markov chain sampling approach
AU - Nakatsuma, Teruo
PY - 2000/3
Y1 - 2000/3
N2 - We develop a Markov chain Monte Carlo method for a linear regression model with an ARMA(p, q)-GARCH(r, s) error. To generate a Monte Carlo sample from the joint posterior distribution, we employ a Markov chain sampling with the Metropolis-Hastings algorithm. As illustration, we estimate an ARMA-GARCH model of simulated time series data.
AB - We develop a Markov chain Monte Carlo method for a linear regression model with an ARMA(p, q)-GARCH(r, s) error. To generate a Monte Carlo sample from the joint posterior distribution, we employ a Markov chain sampling with the Metropolis-Hastings algorithm. As illustration, we estimate an ARMA-GARCH model of simulated time series data.
KW - ARMA process
KW - Bayesian inference
KW - GARCH
KW - Markov chain Monte Carlo
KW - Metropolis-Hastings algorithm
UR - http://www.scopus.com/inward/record.url?scp=0347318519&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0347318519&partnerID=8YFLogxK
U2 - 10.1016/S0304-4076(99)00029-9
DO - 10.1016/S0304-4076(99)00029-9
M3 - Article
AN - SCOPUS:0347318519
VL - 95
SP - 57
EP - 69
JO - Journal of Econometrics
JF - Journal of Econometrics
SN - 0304-4076
IS - 1
ER -