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

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  • 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
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    References listed on IDEAS

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    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)

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