IDEAS home Printed from https://ideas.repec.org/a/eee/jrpoli/v72y2021ics0301420721000891.html
   My bibliography  Save this article

Can Google Trends improve the marble demand model: A case study of USA's marble demand from Turkey

Author

Listed:
  • Başyiğit, Mikail

Abstract

The United States is the second largest mineral importer from Turkey and marble is the most imported mineral commodity. Therefore, understanding the demand function of marble is an important subject for countries such as Turkey. Marble's end-use by individuals and households distinguishes it from other mineral commodities. Like other end-use products, trends and popularity are important factors in determining the demand for marble. The United States' preference for single-family homes, and the customization of these homes by households, increases the importance of the marble. Demand studies in the field of mineral commodities have thus far only focused on demand factors such as GDP per capita. However, they have overlooked the effect of the “trends”. To determine the effect of trends on marble demand, a base model was developed using parity, GDP per capita, and price. The base model was statistically significant and had a relatively high explanatory power (R2 = 0.85). A second model was developed to include the trends variable, obtained from Google Trends. The addition of trends improved the analysis indicators of the base model (R2 = 0.90). This study aims to put forth the effect of “trends” on the marble demand. The presented findings of this paper add to our previously limited understanding of trends and popularity on mineral commodities.

Suggested Citation

  • Başyiğit, Mikail, 2021. "Can Google Trends improve the marble demand model: A case study of USA's marble demand from Turkey," Resources Policy, Elsevier, vol. 72(C).
    • Handle: RePEc:eee:jrpoli:v:72:y:2021:i:c:s0301420721000891
    DOI: 10.1016/j.resourpol.2021.102073
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301420721000891
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.resourpol.2021.102073?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. Baur, Dirk G. & Dimpfl, Thomas, 2016. "Googling gold and mining bad news," Resources Policy, Elsevier, vol. 50(C), pages 306-311.
    2. Salisu, Afees A. & Ogbonna, Ahamuefula E. & Adewuyi, Adeolu, 2020. "Google trends and the predictability of precious metals," Resources Policy, Elsevier, vol. 65(C).
    3. Jain, Anshul & Biswal, Pratap Chandra, 2019. "Does internet search interest for gold move the gold spot, stock and exchange rate markets? A study from India," Resources Policy, Elsevier, vol. 61(C), pages 501-507.
    4. Aharon, David Y. & Qadan, Mahmoud, 2018. "What drives the demand for information in the commodity market?," Resources Policy, Elsevier, vol. 59(C), pages 532-543.
    5. Park, Sungjun & Kim, Jinsoo, 2018. "The effect of interest in renewable energy on US household electricity consumption: An analysis using Google Trends data," Renewable Energy, Elsevier, vol. 127(C), pages 1004-1010.
    6. Nagao, Shintaro & Takeda, Fumiko & Tanaka, Riku, 2019. "Nowcasting of the U.S. unemployment rate using Google Trends," Finance Research Letters, Elsevier, vol. 30(C), pages 103-109.
    7. Lu, Quanying & Li, Yuze & Chai, Jian & Wang, Shouyang, 2020. "Crude oil price analysis and forecasting: A perspective of “new triangle”," Energy Economics, Elsevier, vol. 87(C).
    Full references (including those not matched with items on IDEAS)

    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. Khaskheli, Asadullah & Zhang, Hongyu & Raza, Syed Ali & Khan, Komal Akram, 2022. "Assessing the influence of news indicator on volatility of precious metals prices through GARCH-MIDAS model: A comparative study of pre and during COVID-19 period," Resources Policy, Elsevier, vol. 79(C).
    2. Miao, Miao & Khaskheli, Asadullah & Raza, Syed Ali & Yousufi, Sara Qamar, 2022. "Using internet search keyword data for predictability of precious metals prices: Evidence from non-parametric causality-in-quantiles approach," Resources Policy, Elsevier, vol. 75(C).
    3. Sanjay Sehgal & Neharika Sobti & Florent Diesting, 2021. "Who leads in intraday gold price discovery and volatility connectedness: Spot, futures, or exchange‐traded fund?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(7), pages 1092-1123, July.
    4. Piccoli, Pedro & de Castro, Jessica, 2021. "Attention-return relation in the gold market and market states," Resources Policy, Elsevier, vol. 74(C).
    5. Gao, Feng & Chi, Hong & Shao, Xueyan, 2021. "Forecasting residential electricity consumption using a hybrid machine learning model with online search data," Applied Energy, Elsevier, vol. 300(C).
    6. Salisu, Afees A. & Ogbonna, Ahamuefula E. & Adewuyi, Adeolu, 2020. "Google trends and the predictability of precious metals," Resources Policy, Elsevier, vol. 65(C).
    7. Tsai, I-Chun & Chen, Han-Bo & Lin, Che-Chun, 2024. "The ability of energy commodities to hedge the dynamic risk of epidemic black swans," Resources Policy, Elsevier, vol. 89(C).
    8. Ramos, Sofia B. & Latoeiro, Pedro & Veiga, Helena, 2020. "Limited attention, salience of information and stock market activity," Economic Modelling, Elsevier, vol. 87(C), pages 92-108.
    9. Sheng‐Tun Li & Kuei‐Chen Chiu & Chien‐Chang Wu, 2023. "Apply big data analytics for forecasting the prices of precious metals futures to construct a hedging strategy for industrial material procurement," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(2), pages 942-959, March.
    10. Paulo Ferreira & Éder J.A.L. Pereira & Hernane B.B. Pereira, 2020. "From Big Data to Econophysics and Its Use to Explain Complex Phenomena," JRFM, MDPI, vol. 13(7), pages 1-10, July.
    11. Raifu, Isiaka Akande & Ogbonna, Ahamuefula E, 2021. "Safe-haven Effectiveness of Cryptocurrency: Evidence from Stock Markets of COVID-19 worst-hit African Countries," MPRA Paper 113139, University Library of Munich, Germany.
    12. Lin, Chia-Yang & Chau, Ka Yin & Tran, Trung Kien & Sadiq, Muhammad & Van, Le & Hien Phan, Thi Thu, 2022. "Development of renewable energy resources by green finance, volatility and risk: Empirical evidence from China," Renewable Energy, Elsevier, vol. 201(P1), pages 821-831.
    13. He, Mengxi & Zhang, Yaojie & Wen, Danyan & Wang, Yudong, 2021. "Forecasting crude oil prices: A scaled PCA approach," Energy Economics, Elsevier, vol. 97(C).
    14. Jiang, Ping & Liu, Zhenkun & Wang, Jianzhou & Zhang, Lifang, 2021. "Decomposition-selection-ensemble forecasting system for energy futures price forecasting based on multi-objective version of chaos game optimization algorithm," Resources Policy, Elsevier, vol. 73(C).
    15. Yong Jiang & Yi-Shuai Ren & Chao-Qun Ma & Jiang-Long Liu & Basil Sharp, 2018. "Does the price of strategic commodities respond to U.S. Partisan Conflict?," Papers 1810.08396, arXiv.org, revised Feb 2020.
    16. Afees A. Salisu & Rangan Gupta & Ahamuefula E. Ogbonna & Mark E. Wohar, 2022. "Uncertainty and predictability of real housing returns in the United Kingdom: A regional analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1525-1556, November.
    17. Clément Cariou & Amélie Charles & Olivier Darné, 2024. "Are national or regional surveys useful for nowcasting regional jobseekers? The case of the French region of Pays‐de‐la‐Loire," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 2341-2357, September.
    18. Ngo Thai Hung, 2020. "Analysis of the Time-frequency Connectedness between Gold Prices, Oil Prices and Hungarian Financial Markets," International Journal of Energy Economics and Policy, Econjournals, vol. 10(4), pages 51-59.
    19. Abdollahi, Hooman & Ebrahimi, Seyed Babak, 2020. "A new hybrid model for forecasting Brent crude oil price," Energy, Elsevier, vol. 200(C).
    20. Bjarni G. Einarsson, 2024. "Online Monitoring of Policy Optimality," Economics wp95, Department of Economics, Central bank of Iceland.

    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:72:y:2021:i:c:s0301420721000891. 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.