Analysis and Approximation of a Stochastic Growth Model with Extinction
Fabien Campillo (),
Marc Joannides () and
Irène Larramendy-Valverde ()
Additional contact information
Fabien Campillo: Project-Team MODEMIC, INRIA/INRA, UMR MISTEA
Marc Joannides: Project-Team MODEMIC, INRIA/INRA, UMR MISTEA
Irène Larramendy-Valverde: Université Montpellier 2/I3M
Methodology and Computing in Applied Probability, 2016, vol. 18, issue 2, 499-515
Abstract:
Abstract We consider a stochastic growth model for which extinction eventually occurs almost surely. The associated complete Fokker–Planck equation describing the law of the process is established and studied. This equation combines a PDE and an ODE, connected one to each other. We then design a finite differences numerical scheme under a probabilistic viewpoint. The model and its approximation are evaluated through numerical simulations.
Keywords: Logistic model; Markov processes; Diffusion processes; Extinction; Fokker–Planck equation; PDE; 60J60; 60H35; 65C20; 92D40 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)
Downloads: (external link)
http://link.springer.com/10.1007/s11009-015-9438-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
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:spr:metcap:v:18:y:2016:i:2:d:10.1007_s11009-015-9438-7
Ordering information: This journal article can be ordered from
https://www.springer.com/journal/11009
DOI: 10.1007/s11009-015-9438-7
Access Statistics for this article
Methodology and Computing in Applied Probability is currently edited by Joseph Glaz
More articles in Methodology and Computing in Applied Probability from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().