Relevant States and Memory in Markov Chain Bootstrapping and Simulation

Roy Cerqueti, Paolo Falbo and Cristian Pelizzari

MPRA Paper from University Library of Munich, Germany

Abstract: Markov chain theory is proving to be a powerful approach to bootstrap highly nonlinear time series. In this work we provide a method to estimate the memory of a Markov chain (i.e. its order) and to identify its relevant states. In particular the choice of memory lags and the aggregation of irrelevant states are obtained by looking for regularities in the transition probabilities. Our approach is based on an optimization model. More specifically we consider two competing objectives that a researcher will in general pursue when dealing with bootstrapping: preserving the “structural” similarity between the original and the simulated series and assuring a controlled diversification of the latter. A discussion based on information theory is developed to define the desirable properties for such optimal criteria. Two numerical tests are developed to verify the effectiveness of the method proposed here.

Keywords: Bootstrapping; Information Theory; Markov chains; Optimization; Simulation. (search for similar items in EconPapers)
JEL-codes: C15 C61 C63 C65 (search for similar items in EconPapers)
Date: 2013
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/46250/1/MPRA_paper_46250.pdf original version (application/pdf)
https://mpra.ub.uni-muenchen.de/46254/1/MPRA_paper_46250.pdf revised version (application/pdf)

Related works:
Journal Article: Relevant states and memory in Markov chain bootstrapping and simulation (2017)
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:46250

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 ().