Finite State Markov-Chain Approximations to Highly Persistent Processes

Karen Kopecky and Richard M. H. Suen

MPRA Paper from University Library of Munich, Germany

Abstract: This paper re-examines the Rouwenhorst method of approximating first-order autoregressive processes. This method is appealing because it can match the conditional and unconditional mean, the conditional and unconditional variance and the first-order autocorrelation of any AR(1) process. This paper provides the first formal proof of this and other results. When comparing to five other methods, the Rouwenhorst method has the best performance in approximating the business cycle moments generated by the stochastic growth model. In addition, when the Rouwenhorst method is used, moments computed directly off the stationary distribution are as accurate as those obtained using Monte Carlo simulations.

Keywords: Numerical Methods; Finite State Approximations; Optimal Growth Model (search for similar items in EconPapers)
JEL-codes: C63 (search for similar items in EconPapers)
Date: 2009-05-08
New Economics Papers: this item is included in nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (52)

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/17201/3/MPRA_paper_17201.pdf original version (application/pdf)

Related works:
Journal Article: Finite State Markov-chain Approximations to Highly Persistent Processes (2010)
Working Paper: Finite State Markov-Chain Approximations to Highly Persistent Processes (2009)
Working Paper: Finite State Markov-Chain Approximations to Highly Persistent Processes (2009)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:17201

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().