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Nowcasting with Google Trends in an Emerging Market

Citations

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Cited by:

  1. Havranek, Tomas & Zeynalov, Ayaz, 2018. "Forecasting Tourist Arrivals with Google Trends and Mixed Frequency Data," EconStor Preprints 187420, ZBW - Leibniz Information Centre for Economics.
  2. Tomas Havranek & Ayaz Zeynalov, 2021. "Forecasting tourist arrivals: Google Trends meets mixed-frequency data," Tourism Economics, , vol. 27(1), pages 129-148, February.
  3. Bantis, Evripidis & Clements, Michael P. & Urquhart, Andrew, 2023. "Forecasting GDP growth rates in the United States and Brazil using Google Trends," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1909-1924.
  4. Nikolaos Askitas & Klaus F. Zimmermann, 2015. "The internet as a data source for advancement in social sciences," International Journal of Manpower, Emerald Group Publishing Limited, vol. 36(1), pages 2-12, April.
  5. Laurent Ferrara & Anna Simoni, 2023. "When are Google Data Useful to Nowcast GDP? An Approach via Preselection and Shrinkage," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(4), pages 1188-1202, October.
  6. Paul Gift, 2020. "Moving the Needle in MMA: On the Marginal Revenue Product of UFC Fighters," Journal of Sports Economics, , vol. 21(2), pages 176-209, February.
  7. Jiam Song & Kwangmin Jung & Jonghun Kam, 2023. "Evidence of the time-varying impacts of the COVID-19 pandemic on online search activities relating to shopping products in South Korea," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-12, December.
  8. Hasanzadeh, Samira & Alishahi, Modjgan, 2020. "COVID-19 Pounds: Quarantine and Weight Gain," MPRA Paper 103074, University Library of Munich, Germany.
  9. Fantazzini, Dean & Toktamysova, Zhamal, 2015. "Forecasting German car sales using Google data and multivariate models," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 97-135.
  10. Eric W. K. See-To & Eric W. T. Ngai, 2018. "Customer reviews for demand distribution and sales nowcasting: a big data approach," Annals of Operations Research, Springer, vol. 270(1), pages 415-431, November.
  11. Drachal, Krzysztof, 2018. "Comparison between Bayesian and information-theoretic model averaging: Fossil fuels prices example," Energy Economics, Elsevier, vol. 74(C), pages 208-251.
  12. Karaman Örsal, Deniz Dilan, 2021. "Onlinedaten und Konsumentscheidungen: Voraussagen anhand von Daten aus Social Media und Suchmaschinen," Edition HWWI: Chapters, in: Straubhaar, Thomas (ed.), Neuvermessung der Datenökonomie, volume 6, pages 157-172, Hamburg Institute of International Economics (HWWI).
  13. Rafael R. S. Guimaraes, 2022. "Deep Learning Macroeconomics," Papers 2201.13380, arXiv.org.
  14. Zeynalov, Ayaz, 2014. "Nowcasting Tourist Arrivals to Prague: Google Econometrics," MPRA Paper 60945, University Library of Munich, Germany.
  15. Kohns, David & Bhattacharjee, Arnab, 2023. "Nowcasting growth using Google Trends data: A Bayesian Structural Time Series model," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1384-1412.
  16. Zeynalov, Ayaz, 2017. "Forecasting Tourist Arrivals in Prague: Google Econometrics," MPRA Paper 83268, University Library of Munich, Germany.
  17. Dimitrios Anastasiou & Konstantinos Drakos, 2021. "Nowcasting the Greek (semi‐) deposit run: Hidden uncertainty about the future currency in a Google search," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 1133-1150, January.
  18. Li, Xin & Ma, Jian & Wang, Shouyang & Zhang, Xun, 2015. "How does Google search affect trader positions and crude oil prices?," Economic Modelling, Elsevier, vol. 49(C), pages 162-171.
  19. Ladislav Kristoufek, 2013. "Can Google Trends search queries contribute to risk diversification?," Papers 1310.1444, arXiv.org.
  20. Olivier Gergaud & Victor Ginsburgh, 2016. "Evaluating the Economic Effects of Cultural Events," Working Papers ECARES ECARES 2016-24, ULB -- Universite Libre de Bruxelles.
  21. Chong, Terence Tai Leung & Wu, Zhang & Liu, Yuchen, 2019. "Market Reaction to iPhone Rumors," MPRA Paper 92014, University Library of Munich, Germany.
  22. Gantiah Wuryandani & Indri Mardiani, 2015. "Surveys as leading information to support central bank policy formulation: the case of Indonesia," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Indicators to support monetary and financial stability analysis: data sources and statistical methodologies, volume 39, Bank for International Settlements.
  23. Steven L. Scott & Hal R. Varian, 2015. "Bayesian Variable Selection for Nowcasting Economic Time Series," NBER Chapters, in: Economic Analysis of the Digital Economy, pages 119-135, National Bureau of Economic Research, Inc.
  24. Matthew Brook O’Donnell & Emily B. Falk, 2015. "Big Data under the Microscope and Brains in Social Context," The ANNALS of the American Academy of Political and Social Science, , vol. 659(1), pages 274-289, May.
  25. Kevin Caves & Ted Tatos & Augustus Urschel, 2022. "Are the Lowest-Paid UFC Fighters Really Overpaid? A Comment on Gift (2019)," Journal of Sports Economics, , vol. 23(3), pages 355-365, April.
  26. Zhao, Lu-Tao & Zheng, Zhi-Yi & Wei, Yi-Ming, 2023. "Forecasting oil inventory changes with Google trends: A hybrid wavelet decomposer and ARDL-SVR ensemble model," Energy Economics, Elsevier, vol. 120(C).
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