Persistent Link:
http://hdl.handle.net/10150/184653
Title:
Innovative methods for long-term mineral forecasting
Author:
Jeon, Gyoo Jeong
Issue Date:
1989
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
This study presents improved methods for long-term forecasting of mineral demands. Intensity of use, both in its simple, original form and as described by richer economic relations is one such method, particularly when intensity of use is estimated using rigorous statistical methods. Additionally, this dissertation explores the implications of the learning curve for long term forecasting of mineral demands. This study begins by considering the empirical evidence which applies when a learning curve is present. Then, if a learning pattern is present, the learning model is used to examine an economic measure for specified levels of economic activity. This dissertation also provides some empirical results on the learning curve in mineral industries and demonstrates how the learning model can be used as an economic forecasting tool. As an alternative to the intensity of use and learning models, there is a vector model, either using time varying coefficients or expressed as a transcendental function, to capture dynamics. This model estimates the time varying parameters from the vector space instead of the variable space. The major advantage of this model is that it honors correlations between variables. This is especially important in ex ante forecasting in which explanatory variables themselves must be forecast to obtain a forecast of the dependent variable.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Mineral industries -- Forecasting.; Supply and demand.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Mining and Geological Engineering; Graduate College
Degree Grantor:
University of Arizona
Advisor:
Harris, DeVerle

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleInnovative methods for long-term mineral forecastingen_US
dc.creatorJeon, Gyoo Jeongen_US
dc.contributor.authorJeon, Gyoo Jeongen_US
dc.date.issued1989en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractThis study presents improved methods for long-term forecasting of mineral demands. Intensity of use, both in its simple, original form and as described by richer economic relations is one such method, particularly when intensity of use is estimated using rigorous statistical methods. Additionally, this dissertation explores the implications of the learning curve for long term forecasting of mineral demands. This study begins by considering the empirical evidence which applies when a learning curve is present. Then, if a learning pattern is present, the learning model is used to examine an economic measure for specified levels of economic activity. This dissertation also provides some empirical results on the learning curve in mineral industries and demonstrates how the learning model can be used as an economic forecasting tool. As an alternative to the intensity of use and learning models, there is a vector model, either using time varying coefficients or expressed as a transcendental function, to capture dynamics. This model estimates the time varying parameters from the vector space instead of the variable space. The major advantage of this model is that it honors correlations between variables. This is especially important in ex ante forecasting in which explanatory variables themselves must be forecast to obtain a forecast of the dependent variable.en_US
dc.description.noteDigitization note: p. 96 missing from both paper original and microfilm version; appears to be pagination error.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectMineral industries -- Forecasting.en_US
dc.subjectSupply and demand.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineMining and Geological Engineeringen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorHarris, DeVerleen_US
dc.contributor.committeememberRieber, Michaelen_US
dc.contributor.committeememberNewcomb, Richard T.en_US
dc.contributor.committeememberBerry, James W.en_US
dc.identifier.proquest8915963en_US
dc.identifier.oclc702125070en_US
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