Performance of different models in iron ore price prediction during the time of commodity price spike

Yoochan Kim, Apurna Ghosh, Erkan Topal and Ping Chang

Resources Policy, 2023, vol. 80, issue C

Abstract: Future prediction of commodity price based on available data is very important for mining investors and operators. Commodity prices cointegrate and show Granger causality to and from one another. This research reviewed five different estimation techniques which are Bivariate Non-Linear Regression (BNLR), Multiple Linear Regression (MLR), Multiple Non-Linear Regression (MNLR) as well as logsig and tansig model of Levenberg-Marquardt Artificial Neural Network modelling to simulate the future iron ore price based on 12 other monthly commodity prices and indices including LNG, aluminium, nickel, silver, Australian coal, zinc, gold, oil, tin, copper, lead, and Commodity Price Index (Metals).

Keywords: Iron ore price; Artificial neural network; Levenberg-Marquardt (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jrpoli:v:80:y:2023:i:c:s0301420722006808

DOI: 10.1016/j.resourpol.2022.103237

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