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    Innovative methods for long-term mineral forecasting

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    Author
    Jeon, Gyoo Jeong
    Issue Date
    1989
    Keywords
    Mineral industries -- Forecasting.
    Supply and demand.
    Advisor
    Harris, DeVerle
    
    Metadata
    Show full item record
    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)
    Degree Name
    Ph.D.
    Degree Level
    doctoral
    Degree Program
    Mining and Geological Engineering
    Graduate College
    Degree Grantor
    University of Arizona
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