Multivariate forecasting of mineral commodities prices: Implications for natural resource scarcity.

Persistent Link:
http://hdl.handle.net/10150/187474
Title:
Multivariate forecasting of mineral commodities prices: Implications for natural resource scarcity.
Author:
Sow, Thierno Sadou.
Issue Date:
1996
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:
A comprehensive methodology is developed in this study to assess the scarcity of nonrenewable natural resources. The commodities examined in the study are: aluminum, copper, lead, zinc, tin, molybdenum, steel, oil, and coal. Multivariate state space and extensive learning model methods are used to project real prices of mineral resource products to the year 2011. The methodology is conceived in multivariate dimension to accommodate economic theory of price formation; namely price of a commodity is dependent on prices of complement and substitute products in equilibrium market conditions of supply and demand. Contrary to previous studies which examined each commodity price separately, this study employs simultaneous forecasting of economic variables theorized to be determinants of commodity prices. The multivariate state space technique uses annual data and thus focuses implicitly on disaggregated economic relations. The extensive system of learning models uses cumulative data, and thus facuses on major trends and patterns in economic relations. These techniques are used to assess economic scarcity of mineral resources by means of "holistic" forecasts of commodity price trends. On the basis of projected trends to the year 2011, the study found that all commodities prices are either declining or flat; thus indicating a general failure to support a claim of increasing scarcity of mineral resources. However, observation of the entire price series for some commodities reveals increasing trends in prices, and thus increasing economic scarcity. Conflicting scarcity signals are examined, interpreted, and qualified in light of economic realities of each commodity market, such as the possible influence of market structure and non-market forces interferences on price trends.
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
Committee Chair:
Harris, DeVerle P.

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleMultivariate forecasting of mineral commodities prices: Implications for natural resource scarcity.en_US
dc.creatorSow, Thierno Sadou.en_US
dc.contributor.authorSow, Thierno Sadou.en_US
dc.date.issued1996en_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.abstractA comprehensive methodology is developed in this study to assess the scarcity of nonrenewable natural resources. The commodities examined in the study are: aluminum, copper, lead, zinc, tin, molybdenum, steel, oil, and coal. Multivariate state space and extensive learning model methods are used to project real prices of mineral resource products to the year 2011. The methodology is conceived in multivariate dimension to accommodate economic theory of price formation; namely price of a commodity is dependent on prices of complement and substitute products in equilibrium market conditions of supply and demand. Contrary to previous studies which examined each commodity price separately, this study employs simultaneous forecasting of economic variables theorized to be determinants of commodity prices. The multivariate state space technique uses annual data and thus focuses implicitly on disaggregated economic relations. The extensive system of learning models uses cumulative data, and thus facuses on major trends and patterns in economic relations. These techniques are used to assess economic scarcity of mineral resources by means of "holistic" forecasts of commodity price trends. On the basis of projected trends to the year 2011, the study found that all commodities prices are either declining or flat; thus indicating a general failure to support a claim of increasing scarcity of mineral resources. However, observation of the entire price series for some commodities reveals increasing trends in prices, and thus increasing economic scarcity. Conflicting scarcity signals are examined, interpreted, and qualified in light of economic realities of each commodity market, such as the possible influence of market structure and non-market forces interferences on price trends.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)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.chairHarris, DeVerle P.en_US
dc.contributor.committeememberRieber, Michaelen_US
dc.contributor.committeememberNewcomb, Richard T.en_US
dc.contributor.committeememberMarsh, Stuart E.en_US
dc.identifier.proquest9626499en_US
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