A Markov-Chain Sampling Algorithm for GARCH Models
Studies in Nonlinear Dynamics & Econometrics, 1998, vol. 3, issue 2, 1-13
This paper describes a GAUSS program of a Markov-chain sampling algorithm for GARCH models proposed by Nakatsuma (1998). This algorithm allows us to generate Monte Carlo samples of parameters in a GARCH model from their joint posterior distribution. The samples obtained by this algorithm are used for Bayesian analysis of the GARCH model. As numerical examples, GARCH models of simulated data and of weekly foreign exchange rate series are estimated and analyzed.
References: Add references at CitEc
Citations: View citations in EconPapers (11) Track citations by RSS feed
Downloads: (external link)
https://www.degruyter.com/view/j/snde.1998.3.2/snd ... .1043.xml?format=INT (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:bpj:sndecm:v:3:y:1998:i:2:n:al1
Ordering information: This journal article can be ordered from
Access Statistics for this article
Studies in Nonlinear Dynamics & Econometrics is currently edited by Bruce Mizrach
More articles in Studies in Nonlinear Dynamics & Econometrics from De Gruyter
Bibliographic data for series maintained by Peter Golla ().