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Forecasting Iron-ore Prices Using Multi-Regression Model
Date Issued
2010
Date
2010
Author(s)
Bunker, Robin
Abstract
In order to develop a regression model to forecast iron-ore price, CVRD & Baosteel annual contract iron ore prices were used for IOF price i.e., the dependent variable. Twelve factors were identified to have influence on the IOF price as independent variables in the regression model.
In the process of developing Multi Regression Model, assumptions of – Linearity, Independence, Normality and Equal Variance were tested. It was found that multicollinearity among independent variables was the main problem. Stepwise regression was proposed to resolve this. The stepwise procedures successfully solved the problem of multicollinearity by reducing the total number of independent variables to four. The variables selected by stepwise regression were Oil Price, Production of Steel in China, World Steel Exports and China Iron Ore Production. The adjusted coefficient of correlation remained almost same.
The results obtained with Stepwise Regression Model were very encouraging for years 2006-2007 and further research was suggested to overcome certain limitations.
In the process of developing Multi Regression Model, assumptions of – Linearity, Independence, Normality and Equal Variance were tested. It was found that multicollinearity among independent variables was the main problem. Stepwise regression was proposed to resolve this. The stepwise procedures successfully solved the problem of multicollinearity by reducing the total number of independent variables to four. The variables selected by stepwise regression were Oil Price, Production of Steel in China, World Steel Exports and China Iron Ore Production. The adjusted coefficient of correlation remained almost same.
The results obtained with Stepwise Regression Model were very encouraging for years 2006-2007 and further research was suggested to overcome certain limitations.
Subjects
Iron-ore
Multi-Regression Model
Forecasting
Type
thesis
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Name
ntu-99-R97749059-1.pdf
Size
23.32 KB
Format
Adobe PDF
Checksum
(MD5):db4590b35fba91f7e6c17130c57ed96a