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Econometric model of iron ore through principal component analysis and multiple linear regression
Bárbara Isabela DA Silva Campos1, Gisele C A Lopes1, Philipe S C DE Castro1
1Programa de Pós-Graduação em Engenharia Mineral, Universidade Federal de Ouro Preto, Departamento de Engenharia de Minas, Campus Universitário, s/n, Morro do Cruzeiro, 35400-000 Ouro Preto, MG, Brazil.
Anais Da Academia Brasileira De Ciencias
|May 10, 2023
Summary
Iron ore prices fluctuate due to supply and demand imbalances, impacting global economies. Key factors influencing iron ore prices include Brazilian exports, Chinese steel production, and coal prices.
Area of Science:
- Economics
- Commodity Markets
- Econometrics
Background:
- Iron ore price volatility significantly impacts global economic stability and mineral enterprise viability.
- Microeconomic imbalances between supply and demand create significant market swings and economic consequences.
Purpose of the Study:
- To identify and evaluate key market variables influencing iron ore prices.
- To apply multivariate statistical techniques for a comprehensive market analysis.
Main Methods:
- Multivariate statistical techniques were employed, specifically Principal Component Analysis (PCA) and Multiple Linear Regression (MLR).
- Analysis included variables such as Brazilian iron ore exports, Chinese and Indian steel production, coal prices, and steel prices.
Main Results:
- The first three principal components explained 89.12% of the data variability, indicating strong underlying factors.
- Multiple linear regression identified Brazilian iron ore exports, Chinese steel production, coal price, Indian steel production, and steel price as significant influencing variables.
Conclusions:
- The study successfully identified key drivers of iron ore price fluctuations.
- Understanding these variables is crucial for stakeholders in the iron ore and steel industries to navigate market dynamics.
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