Agalega, E. & Antwi, S. (2013). The impact of macroeconomic variables on gross domestic product: empirical evidence from Ghana,
International Business Research, 6(5), 108. DOI:
10.5539/ibr.v6n5p108
Central Bank of the Islamic Republic of Iran. (2023). Economic Research and Policy Department, Economic Time Series Database, Time |Series of Unofficial Exchange Rates, Tehran. (In Persian). Available:
https://tsd.cbi.ir/Display/Content.aspx
Chen, J., Jin, F., Ouyang, G., Ouyang, J., & Wen, F. (2019). Oil price shocks, economic policy uncertainty, and industrial economic growth in China. PloS one, 14(5), e0215397. https://doi.org/10.1371/journal.pone.0215397
Consumers and Producers Protection Organization. (2023). The Monthly Price Time Series of Various Oils, Ministry of Industry, Mine and Trade, Tehran. (In Persian), Available:
https://cppo.mimt.gov.ir/
Dutta, A. (2018). Impacts of oil volatility shocks on metal markets: a research note. Resources Policy, 55, 9-19. https://doi.org/10.1016/j.resourpol.2017.09.003
Farajian, P. & Farajian, N. (2022). Global iron ore price forecasting using neural networks. Quarterly Journal of System and Productivity Engineering, 1(4), 113-126, (In Persian).
https://systems.eyc.ac.ir/article_243419.html
Firdaus, A. & Amrina, U. (2023). Modeling the Price Forecast for Construction Steel: A Case Study in EPC Company.
E3S Web of Conferences 399, 03020. DOI:
10.1051/e3sconf/202339903020
Gudarzi, Hossein. (2007). Forecasting Iran's crude steel demand in 2021.
The Journal of Planning and Budgeting, 4, 209-232 (In Persian). DOI:
20.1001.1.22519092.1386.12.4.3.4
Hu, D. (2010). Analysis of Influences from Exchange Rate to Pricing of China Steel Industry.
International Conference on System Science and Engineering, Texas A&M International University Laredo, Texas.
DOI: 10.1109/ICSSE.2010.5551825
Kazemian, Mina, Afshar Kazemi, Mohammad Ali, Fathi Hafshejani, Kiamars, & Motadel, Mohammadreza. (2023). Determining the Optimal Price in the Steel Industry Using Multilateral Monopoly Patterns with the Approach of Neural Networks and Game Theory. Industrial Management Studies, 21(68), 35-66 (In Persian). DOI: 10.22054/jims.2023.68936. 2798
Khojamli, A. Janfada, M. & Takhmchi, B. (2010). Iron ore price forecasting using time series.
29th Earth Sciences Conference, Geological Survey & Mineral Exploration of Iran, (In Persian).
https://gsi.ir/fa/articles/10310/
Korhonen, I., & Ledyaeva, S. (2010). Trade linkages and macroeconomic effects of the price of oil. Energy Economics, 32(4), 848-856. https://doi.org/10.1016/j.eneco.2009.11.005
Liu, Y. Li, H. Guan, J. Liu, X. Guan, Q. & Sun, Q. (2019). Influence of different factors on prices of upstream, middle and downstream products in China's whole steel industry chain: Based on Adaptive Neural Fuzzy Inference System.
Resources Policy, 60, 134-142.
https://doi.org/10.1016/j.resourpol.2018.12.009
Malanichev, A. G. & Vorobyev, P. V. (2011). Forecast of global steel prices.
Studies on Russian Economic Development, 3, 304 – 311. DOI:
10.1134/S1075700711030105
Ministry of Energy. (2023). Monthly report of water and electricity industry statistics.
Deputy Research and Human Resources, Tehran, (In Persian), Available:
https://isn.moe.gov.ir/
Mohammadi, A. Soltani, S. & Bakhshandeh, H. (2012). Iron ore price forecasting using a time series model.
International Conference on Mining Engineering, Metallurgy and Environment, Zanjan, 88-92, (In Persian).
https://www.magiran.com/paper/1234314/
Ratti, R. A., & Vespignani, J. L. (2016). Oil prices and global factor macroeconomic variables. Energy Economics, 59, 198-212. https://doi.org/10.1016/j.eneco.2016.06.002
Sari, R., Hammoudeh, S., & Soytas, U. (2010). Dynamics of oil price, precious metal prices, and exchange rate. Energy Economics, 32(2), 351-362. https://doi.org/10.1016/j.eneco.2009.08.010
Shafiei, A. & Mirabi, V. R. (2020). Design and validation of financing models in large companies of the steel industry.
Financial Economics,
14(51), 83-114. (In Persian). DOI:
20.1001.1.25383833.1399.14.51.4.2
Shahzad, S. J. H., Rehman, M. U., & Jammazi, R. (2019). Spillovers from oil to precious metals: quantile approaches. Resources Policy, 61, 508-521. https://doi.org/10.1016/j.resourpol.2018.05.002
Shao, L., & Zhang, H. (2020). The impact of oil price on the clean energy metal prices: A multi-scale perspective. Resources Policy, 68, 101730. https://doi.org/10.1016/j.resourpol.2020.101730
Sims, C. (1980), Macroeconomics and Reality, Econometrica, 48: 1-48. https://doi.org/10.2307/1912017
Singhal, S., Choudhary, S., & Biswal, P. C. (2019). Return and volatility linkages among International crude oil price, gold price, exchange rate, and stock markets: Evidence from Mexico. Resources Policy, 60, 255-261. https://doi.org/10.1016/j.resourpol.2019.01.004
Statistical Centre of Iran. (2023). Definitions and Concepts, Industrial producer price index, Tehran, (In Persian), Available:
https://www.amar.org.ir/
Statistical Centre of Iran. (2023). Industrial producer price index, Tehran. (In Persian). Available:
https://www.amar.org.ir/
Varangis, Panos, Cuncan, Ronald C. (1990). The Response of Japanese and U.S. Steel Prices to Changes in the Yen-Dollar Exchange Rate. Policy, Research, and External Affairs, working Papers: WPS 367, The World Bank.
https://ideas.repec.org/p/wbk/wbrwps/367.html
Wen, F., Xiao, Y., & Wu, H. (2019). The effects of foreign uncertainty shocks on China’s macro-economy: Empirical evidence from a nonlinear ARDL model. Physica A: Statistical Mechanics and its Applications, 532, 121879. https://doi.org/10.1016/j.physa.2019.121879
Yaya, O. S., Ogbonna, A. E., Adesina, O. A., Alobaloke, K. A., & Vo, X. V. (2022). Time-variation between metal commodities and oil, and the impact of oil shocks: GARCH-MIDAS and DCC-MIDAS analyses. Resources Policy, 79, 103036. https://doi.org/10.1016/j.resourpol.2022.103036
Yin, L., & Ma, X. (2018). Causality between oil shocks and exchange rate: a Bayesian, graph-based VAR approach. Physica A: Statistical Mechanics and its Applications, 508, 434-453. https://doi.org/10.1016/j.physa.2018.05.064
Zhang, H. Nguyen, H. Bui, X. Pradhan, B. Mai, N. & Vu, D. (2021). Proposing Two Novel Hybrid Intelligence Models for Forecasting Copper Price Based on Extreme Learning Machine and Meta-Heuristic Algorithms.
Resources Policy, 73, 1-12.
https://doi.org/10.1016/j.resourpol.2021.102195
Zhang, H., Zhu, X., Guo, Y., & Liu, H. (2018). A separate reducedāform volatility forecasting model for the nonferrous metal market: Evidence from copper and aluminum. Journal of Forecasting, 37(7), 754-766. https://doi.org/10.1002/for.2523
Zhu, X., Zheng, W., Zhang, H., & Guo, Y. (2019). Time-varying international market power for the Chinese iron ore markets. Resources Policy, 64, 101502. https://doi.org/10.1016/j.resourpol.2019.101502