Forecasting total natural-gas consumption in Spain by using the stochastic Gompertz innovation diffusion model
R. Gutiérrez,
A. Nafidi and
R. Gutiérrez Sánchez
Applied Energy, 2005, vol. 80, issue 2, 115-124
Abstract:
The principal objective of the present study is to examine the possibilities of using a Gompertz-type innovation diffusion process as a stochastic growth model of natural-gas consumption in Spain, and to compare our results with those obtained, on the one hand, by stochastic logistic innovation modelling and, on the other, by using a stochastic lognormal growth model based on a non-innovation diffusion process. Such a comparison is carried out taking into account the macroeconomic characteristics and natural-gas consumption patterns in Spain, both of which reflect the current expansive situation characterizing the Spanish economy. From the technical standpoint a contribution is also made to the theory of the stochastic Gompertz Innovation diffusion process (SGIDP), as applied to the case in question.
Keywords: Stochastic Gompertz; logistic and lognormal models Natural-gas consumption Estimation and prediction (search for similar items in EconPapers)
Date: 2005
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (41)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306-2619(04)00055-8
Full text for ScienceDirect subscribers only
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:eee:appene:v:80:y:2005:i:2:p:115-124
Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic
Access Statistics for this article
Applied Energy is currently edited by J. Yan
More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().