Review of Methods for Exploratory Data Analysis for Copper Prices 1960-2020

Bell, Peter (2025): Review of Methods for Exploratory Data Analysis for Copper Prices 1960-2020.

Abstract

This paper presents a statistical analysis of the historical data for global copper prices from the World Bank for the period from 1960 to 2025. This paper presents methods and results for several different approaches. The paper uses time-lag diagrams to show price transition periods as a feature of chaos theory established in other research. The paper also shows methods for using percentile rankings along different dimensions—for example, the years where the copper price increased the most. The paper also shows pathwise data analysis methods that use overlapping data samples to generate rankings about the data on a monthly basis, compared with an ensemble of values from all other paths in the dataset. For example, what did the all-time best five years in the copper markets look like based on the trailing-twelve-month price increase? The paper discusses how these methods can be used to make predictions and how the methods can be used for time series of other base metals, like zinc, or unrelated economic statistical time series.

• Item Type: MPRA Paper
• Commentary on: Bell, Peter N (2015): Identifying the Median Path of a Stochastic Processes.
• Language: English
• Keywords: Statistics, Time Series, Methods, Copper, Mining, Finance, World Bank, Exploratory Data Analysis, 1960-2025, Global
• Subjects: A - General Economics and Teaching > A1 - General Economics; A - General Economics and Teaching > A1 - General Economics > A19 - Other; B - History of Economic Thought, Methodology, and Heterodox Approaches > B5 - Current Heterodox Approaches > B59 - Other; C - Mathematical and Quantitative Methods > C0 - General; C - Mathematical and Quantitative Methods > C0 - General > C02 - Mathematical Methods; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C18 - Methodological Issues: General; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C19 - Other; C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics; C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C40 - General; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63 - Computational Techniques ; Simulation Modeling; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C69 - Other; C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs; C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C81 - Methodology for Collecting, Estimating, and Organizing Microeconomic Data ; Data Access; D - Microeconomics > D2 - Production and Organizations; G - Financial Economics > G1 - General Financial Markets; G - Financial Economics > G1 - General Financial Markets > G10 - General; G - Financial Economics > G1 - General Financial Markets > G17 - Financial Forecasting and Simulation; G - Financial Economics > G1 - General Financial Markets > G19 - Other; K - Law and Economics > K2 - Regulation and Business Law; K - Law and Economics > K2 - Regulation and Business Law > K23 - Regulated Industries and Administrative Law; L - Industrial Organization > L2 - Firm Objectives, Organization, and Behavior; L - Industrial Organization > L5 - Regulation and Industrial Policy; L - Industrial Organization > L7 - Industry Studies: Primary Products and Construction; L - Industrial Organization > L7 - Industry Studies: Primary Products and Construction > L72 - Mining, Extraction, and Refining: Other Nonrenewable Resources; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q3 - Nonrenewable Resources and Conservation; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q3 - Nonrenewable Resources and Conservation > Q31 - Demand and Supply ; Prices; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q3 - Nonrenewable Resources and Conservation > Q32 - Exhaustible Resources and Economic Development; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q3 - Nonrenewable Resources and Conservation > Q33 - Resource Booms; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q5 - Environmental Economics; Y - Miscellaneous Categories > Y1 - Data: Tables and Charts; Y - Miscellaneous Categories > Y1 - Data: Tables and Charts > Y10 - Data: Tables and Charts

References

Bell, Peter N, 2015. "Identifying the Median Path Process," MPRA Paper 72680, University Library of Munich, Germany.

Bell, Peter N, 2015. "Method for Comparing Economic Impacts Caused by Government Actions on Natural Resource Extraction in Logging, Oil and Gas, and Mining," MPRA Paper 72680, University Library of Munich, Germany.

Cortez, T., Coulton, C., Sammut, J. (2018), "Determining the chaotic behaviour of copper prices in the long-term using annual price data," Palgrave Commun 4, 8, https://doi.org/10.1057/s41599-017-0060-x

Duvhammar, M. (2018), “Volatility of copper prices and the effect of real interest rate Doesges: does the theory of storage explain the volatility of copper spot and futures prices?” Second cycle, A2E. Uppsala: SLU, Dept. of Economics

Mansukhani, S. (2012), "The Hurst Exponent: Predictability of Time Series,” https://pubsonline.informs.org/do/10.1287/LYTX.2012.04.05/ful,l

Ernanto, W., Faturohman, T. (2025), “Macro and micro factors on global copper pricing: a historical data analysis.” Miner Econ 38, 845–867 (2025). https://doi.org/10.1007/s13563-025-00505-x

Qian, B., Rasheed, K. (2004), “HURST EXPONENT AND FINANCIAL MARKET PREDICTABILITY” https://www.academia.edu/74800827/Hurst_Exponent_and_Financial_Market_Predictability

United States Geological Survey, (2025), “Mineral Industry Survey” https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/media/files/mis-202503-coppe.pdf