IDEAS home Printed from https://ideas.repec.org/a/eee/jrpoli/v63y2019ic29.html
   My bibliography  Save this article

Modeling steel supply and demand functions using logarithmic multiple regression analysis (case study: Steel industry in Iran)

Author

Listed:
  • Mehmanpazir, Farhad
  • Khalili-Damghani, Kaveh
  • Hafezalkotob, Ashkan

Abstract

The steel industry is considered as one of the mother industries that serves many large and small industries. Hence, recognizing the market situation of the steel industry both inside and outside the country is very important. Supply and demand are among the most important factors in stimulating the steel market. Usually, supply and demand have complex function. So, modeling and forecasting steel supply and demand require the use of accurate and scientific approaches. This paper presents an approach to identify the steel supply and demand functions and also to forecast the supply and demand trends. In the first step, through reviewing the historical data on the steel supply and demand in Iran, the effective and most important variables will be identified. Then, the supply and demand functions will be fitted using multiple logarithmic regression analysis. Logarithmically transforming variables in a regression model is a very common way to handle situations where a non-linear relationship exists between the independent and dependent variables. The accuracy of estimations is checked through appropriate statistical tests. The analysis is based on data of Iran steel market obtained from a 60 monthly period starting in 2010 and ending in 2014. The results showed that the estimated functions was appropriate in modeling the steel supply and demand behavior. The extrapolation analysis using 24 monthly data from 2017-2018 has also been accomplished to check the performance of the regression analysis.

Suggested Citation

  • Mehmanpazir, Farhad & Khalili-Damghani, Kaveh & Hafezalkotob, Ashkan, 2019. "Modeling steel supply and demand functions using logarithmic multiple regression analysis (case study: Steel industry in Iran)," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    • Handle: RePEc:eee:jrpoli:v:63:y:2019:i:c:29
    DOI: 10.1016/j.resourpol.2019.101409
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301420719300583
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.resourpol.2019.101409?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Singhal, Shelly & Choudhary, Sangita & Biswal, Pratap Chandra, 2019. "Return and volatility linkages among International crude oil price, gold price, exchange rate and stock markets: Evidence from Mexico," Resources Policy, Elsevier, vol. 60(C), pages 255-261.
    2. Chen, Yibo & Tan, Hongwei, 2017. "Short-term prediction of electric demand in building sector via hybrid support vector regression," Applied Energy, Elsevier, vol. 204(C), pages 1363-1374.
    3. Shakoor Shahabi, Reza & Basiri, Mohammad Hossein & Rashidi Kahag, Mahdi & Ahangar Zonouzi, Samad, 2014. "An ANP–SWOT approach for interdependency analysis and prioritizing the Iran׳s steel scrap industry strategies," Resources Policy, Elsevier, vol. 42(C), pages 18-26.
    4. Guo, Sui & Li, Huajiao & An, Haizhong & Sun, Qingru & Hao, Xiaoqing & Liu, Yanxin, 2019. "Steel product prices transmission activities in the midstream industrial chain and global markets," Resources Policy, Elsevier, vol. 60(C), pages 56-71.
    5. Shahba, Sudabe & Arjmandi, Reza & Monavari, Masoud & Ghodusi, Jamal, 2017. "Application of multi-attribute decision-making methods in SWOT analysis of mine waste management (case study: Sirjan's Golgohar iron mine, Iran)," Resources Policy, Elsevier, vol. 51(C), pages 67-76.
    6. Hailu, Degol & Kipgen, Chinpihoi, 2017. "The Extractives Dependence Index (EDI)," Resources Policy, Elsevier, vol. 51(C), pages 251-264.
    7. Sun, Wei & Dong, Kaiqiang & Zhao, Tianyu, 2017. "Market demand dynamic induced mechanism in China's steel industry," Resources Policy, Elsevier, vol. 51(C), pages 13-21.
    8. Azimifard, Arezoo & Moosavirad, Seyed Hamed & Ariafar, Shahram, 2018. "Selecting sustainable supplier countries for Iran's steel industry at three levels by using AHP and TOPSIS methods," Resources Policy, Elsevier, vol. 57(C), pages 30-44.
    9. Fernandez, Viviana, 2018. "Price and income elasticity of demand for mineral commodities," Resources Policy, Elsevier, vol. 59(C), pages 160-183.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mohammadi, Mir Ahmad & Sayadi, Ahmad Reza & Khoshfarman, Mahsa & Husseinzadeh Kashan, Ali, 2022. "A systems dynamics simulation model of a steel supply chain-case study," Resources Policy, Elsevier, vol. 77(C).
    2. Jun Hao & Xiaolei Sun & Qianqian Feng, 2020. "A Novel Ensemble Approach for the Forecasting of Energy Demand Based on the Artificial Bee Colony Algorithm," Energies, MDPI, vol. 13(3), pages 1-25, January.
    3. Puwei Zhang & Li Wu & Rui Li, 2023. "Development Drivers of Rural Summer Health Tourism for the Urban Elderly: A Demand- and Supply-Based Framework," Sustainability, MDPI, vol. 15(13), pages 1-27, July.
    4. Mehmanpazir, Farhad & Khalili-Damghani, Kaveh & Hafezalkotob, Ashkan, 2022. "Dynamic strategic planning: A hybrid approach based on logarithmic regression, system dynamics, Game Theory and Fuzzy Inference System (Case study Steel Industry)," Resources Policy, Elsevier, vol. 77(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Guo, Sui & Li, Huajiao & An, Haizhong & Sun, Qingru & Hao, Xiaoqing & Liu, Yanxin, 2019. "Steel product prices transmission activities in the midstream industrial chain and global markets," Resources Policy, Elsevier, vol. 60(C), pages 56-71.
    2. Wang, Di & Shen, Ye & Zhao, Yueying & He, Wei & Liu, Xue & Qian, Xiangyan & Lv, Tao, 2020. "Integrated assessment and obstacle factor diagnosis of China's scientific coal production capacity based on the PSR sustainability framework," Resources Policy, Elsevier, vol. 68(C).
    3. Amirshenava, Sina & Osanloo, Morteza, 2022. "Strategic planning of post-mining land uses: A semi-quantitative approach based on the SWOT analysis and IE matrix," Resources Policy, Elsevier, vol. 76(C).
    4. Shuang Yao & Donghua Yu & Yan Song & Hao Yao & Yuzhen Hu & Benhai Guo, 2018. "Dry Bulk Carrier Investment Selection through a Dual Group Decision Fusing Mechanism in the Green Supply Chain," Sustainability, MDPI, vol. 10(12), pages 1-19, November.
    5. K.S., Sujit & Ray, Subhajyoti, 2023. "Linear and nonlinear asymmetric relationship in crude oil, gold, stock market and exchange rates: An evidence from the UAE," Resources Policy, Elsevier, vol. 83(C).
    6. Ozdemir, Ali Can & Buluş, Kurtuluş & Zor, Kasım, 2022. "Medium- to long-term nickel price forecasting using LSTM and GRU networks," Resources Policy, Elsevier, vol. 78(C).
    7. Karasu, Seçkin & Altan, Aytaç, 2022. "Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization," Energy, Elsevier, vol. 242(C).
    8. Tanin, Tauhidul Islam & Sarker, Ashutosh & Brooks, Robert & Do, Hung Xuan, 2022. "Does oil impact gold during COVID-19 and three other recent crises?," Energy Economics, Elsevier, vol. 108(C).
    9. Khan, Muhammad Imran & Teng, Jian-Zhou & Khan, Muhammad Kamran & Jadoon, Arshad Ullah & Khan, Muhammad Fayaz, 2021. "The impact of oil prices on stock market development in Pakistan: Evidence with a novel dynamic simulated ARDL approach," Resources Policy, Elsevier, vol. 70(C).
    10. Zhang, Guangyong & Jiang, Le & Tian, Lixin & Fu, Min, 2021. "Analysis of the gold fixing price fluctuation in different times based on the directed weighted networks," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    11. Aamir Aijaz Syed & Muhammad Abdul Kamal, 2024. "Do cryptocurrency and commodities markets affect stock market performance in South Asia? An empirical investigation during the COVID-19 pandemic," International Journal of Economics and Business Research, Inderscience Enterprises Ltd, vol. 27(4), pages 673-692.
    12. Fan, Cheng & Sun, Yongjun & Xiao, Fu & Ma, Jie & Lee, Dasheng & Wang, Jiayuan & Tseng, Yen Chieh, 2020. "Statistical investigations of transfer learning-based methodology for short-term building energy predictions," Applied Energy, Elsevier, vol. 262(C).
    13. Lebdioui, Amir, 2021. "The Multidimensional Indicator of Extractives-based Development (MINDEX): A new approach to measuring resource wealth and dependence," World Development, Elsevier, vol. 147(C).
    14. Younis, Ijaz & Shah, Waheed Ullah & Yousaf, Imran, 2023. "Static and dynamic linkages between oil, gold and global equity markets in various crisis episodes: Evidence from the Wavelet TVP-VAR," Resources Policy, Elsevier, vol. 80(C).
    15. Guzmán, Juan Ignacio & Karpunina, Alina & Araya, Constanza & Faúndez, Patricio & Bocchetto, Marcela & Camacho, Rodolfo & Desormeaux, Daniela & Galaz, Juanita & Garcés, Ingrid & Kracht, Willy & Lagos, , 2023. "Chile: On the road to global sustainable mining," Resources Policy, Elsevier, vol. 83(C).
    16. Dong, Kaiqiang & Sun, Wei, 2022. "Would the market mechanism cause the formation of overcapacity?: Evidence from Chinese listed firms of manufacturing industry," International Review of Economics & Finance, Elsevier, vol. 79(C), pages 97-113.
    17. Wei, Ziqing & Zhang, Tingwei & Yue, Bao & Ding, Yunxiao & Xiao, Ran & Wang, Ruzhu & Zhai, Xiaoqiang, 2021. "Prediction of residential district heating load based on machine learning: A case study," Energy, Elsevier, vol. 231(C).
    18. David Iheke Okorie & Boqiang Lin, 2022. "Crude oil market and Nigerian stocks: An asymmetric information spillover approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4002-4017, October.
    19. Du, He & Zhang, Chunguang, 2024. "Economic policy uncertainty and natural resources commodity prices: A comparative analysis of pre- and post-pandemic quantile trends in China," Resources Policy, Elsevier, vol. 88(C).
    20. Sarit Maitra & Vivek Mishra & Sukanya Kundu & Manav Chopra, 2023. "Econometric Model Using Arbitrage Pricing Theory and Quantile Regression to Estimate the Risk Factors Driving Crude Oil Returns," Papers 2309.13096, arXiv.org, revised Oct 2023.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jrpoli:v:63:y:2019:i:c:29. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/30467 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.