Uncertainty quantification of world population growth: A self-similar PDF model

Heinz Stefan ()
Additional contact information
Heinz Stefan: Department of Mathematics, University of Wyoming, 1000 East University Avenue, Laramie, WY 82071, USA

Monte Carlo Methods and Applications, 2014, vol. 20, issue 4, 261-277

Abstract: The uncertainty of world population growth represents a serious global problem. Existing methods for quantifying this uncertainty face a variety of questions. An essential problem of these methods is the lack of direct evidence for their validity, for example by means of comparisons with independent observations like measurements. A way to support the validity of such forecast methods is to validate these models with reference models, which play the role of independent observations. Desired properties of such a reference model are formulated here. A new reference world population model is formulated by a probabilistic extension of recent deterministic UN projections. This model is validated in terms of theory and observations: it is shown that the model has all desired properties of a reference model, and its predictions are very well supported by the known world population development from 1980 till 2010. Applications of this model as a reference model demonstrate the advantages of the stochastic world population model presented here.

Keywords: World population growth; UN world population forecasts; uncertainty quantification; stochastic world population model (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/mcma-2014-0005 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

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:bpj:mcmeap:v:20:y:2014:i:4:p:261-277:n:4

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/mcma/html

DOI: 10.1515/mcma-2014-0005

Access Statistics for this article

Monte Carlo Methods and Applications is currently edited by Karl K. Sabelfeld

More articles in Monte Carlo Methods and Applications from De Gruyter
Bibliographic data for series maintained by Peter Golla ().