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Tracking socio-economic activities in European countries with unconventional data

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Published:07 September 2022Publication History

ABSTRACT

This contribution shows our ongoing work aimed at monitoring societal issues and economic activities (e.g., industrial production, unemployment, loneliness, cultural participation) across EU member states mining unconventional data sources to complement official statistics. Considered unconventional data sources include the Global Dataset of Events, Language and Tone (GDELT), Google Search data, and Dow Jones Data, News and Analytics (DNA). We show an early experiment aiming at nowcasting unemployment in Germany, Spain, France, and Italy, demonstrating the added value of these data both for scholars and policymakers.

References

  1. D. Aaronson, S. A. Brave, R. Butters, D. W. Sacks, and B. Seo. 2020. Using the Eye of the Storm to Predict the Wave of Covid-19 UI Claims. Technical Report 2020-10. Federal Reserve Bank of Chicago. https://www.chicagofed.org/~/media/publications/working-papers/2020/wp2020-10-pdf.pdfGoogle ScholarGoogle Scholar
  2. S. B. Aruoba, F. X. Diebold, and C. Scotti. 2009. Real-Time Measurement of Business Conditions. Journal of Business & Economic Statistics 27, 4 (2009), 417–427.Google ScholarGoogle ScholarCross RefCross Ref
  3. S. R. Baker and A. Fradkin. 2017. The impact of unemployment insurance on job search: Evidence from Google search data. Review of Economics and Statistics 99, 5 (2017), 756–768.Google ScholarGoogle ScholarCross RefCross Ref
  4. L. Barbaglia, S. Consoli, and S. Manzan. 2021. Forecasting GDP in Europe with textual data. Available at SSRN 3898680 (2021), 1–38.Google ScholarGoogle Scholar
  5. L. Barbaglia, S. Consoli, and S. Manzan. 2022. Forecasting with Economic News. Journal of Business & Economic Statistics (in press) (2022), 1–12. https://doi.org/10.1080/07350015.2022.2060988Google ScholarGoogle Scholar
  6. L. Barbaglia, S. Consoli, S. Manzan, D. Reforgiato Recupero, M. Saisana, and L. Tiozzo Pezzoli. 2021. Data Science Technologies in Economics and Finance: A Gentle Walk-In. In Data Science for Economics and Finance: Methodologies and Applications. Springer Nature, Switzerland AG, 1–17.Google ScholarGoogle Scholar
  7. L. Barbaglia, L. Frattarolo, L. Onorante, F. Pericoli, M. Ratto, and L. Tiozzo Pezzoli. 2022. Testing Big Data in a Big Crisis: Nowcasting under COVID-19. Working paper available at SSRN 4066479 (2022), 38 pages.Google ScholarGoogle Scholar
  8. D. Borup, D. E. Rapach, and E. C. M. Schütte. 2021. Now-and backcasting initial claims with high-dimensional daily internet search-volume data. CREATES Research Papers 2021-02 (2021), 1–52.Google ScholarGoogle Scholar
  9. J. Bousquet, I. Agache, J. M. Anto, K. C. Bergmann, C. Bachert, I. Annesi-Maesano, P. J. Bousquet, G. D’Amato, P. Demoly, G. De Vries, 2017. Google Trends terms reporting rhinitis and related topics differ in European countries. Allergy 72, 8 (2017), 1261–1266.Google ScholarGoogle ScholarCross RefCross Ref
  10. A. Brodeur, A. E. Clark, S. Flèche, and N. Powdthavee. 2021. COVID-19, Lockdowns and Well-Being: Evidence from Google Trends. Journal of Public Economics 193 (2021), 104346.Google ScholarGoogle ScholarCross RefCross Ref
  11. P. Brunori and G. Resce. 2020. Searching for the peak Google Trends and the Covid-19 outbreak in Italy. Technical Report. IRIS - Università degli Studi del Molise, Italy. https://ssrn.com/abstract=3569909Google ScholarGoogle Scholar
  12. G. Caperna, M. Colagrossi, A. Geraci, and G. Mazzarella. 2022. A babel of web-searches: Googling unemployment during the pandemic. Labour Economics 74(2022), 102097.Google ScholarGoogle ScholarCross RefCross Ref
  13. H. Choi and H. Varian. 2012. Predicting the present with Google Trends. Economic record 88(2012), 2–9.Google ScholarGoogle Scholar
  14. S. Consoli, S. Barbaglia, and S. Manzan. 2022. Fine-grained, aspect-based sentiment analysis on economic and financial lexicon. Knowledge-Based Systems 247 (2022), 108781. https://doi.org/10.1016/j.knosys.2022.108781Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. S. Consoli, L.T. Pezzoli, and E. Tosetti. 2021. Emotions in macroeconomic news and their impact on the European bond market. Journal of International Money and Finance 118 (2021), 102472.Google ScholarGoogle ScholarCross RefCross Ref
  16. S. Consoli, L. Tiozzo Pezzoli, and E. Tosetti. 2022. Neural forecasting of the Italian sovereign bond market with economic news. Journal of the Royal Statistical Society. Series A: Statistics in Society (in press)(2022), 1–28.Google ScholarGoogle Scholar
  17. Z. Da, J. Engelberg, and P. Gao. 2015. The sum of all FEARS investor sentiment and asset prices. The Review of Financial Studies 28, 1 (2015), 1–32.Google ScholarGoogle ScholarCross RefCross Ref
  18. F. D’Amuri and J. Marcucci. 2017. The predictive power of Google searches in forecasting US unemployment. International Journal of Forecasting 33, 4 (2017), 801–816.Google ScholarGoogle ScholarCross RefCross Ref
  19. T. Fetzer, L. Hensel, J. Hermle, and C. Roth. 2020. Coronavirus perceptions and economic anxiety. Review of Economics and Statistics 103, 5 (2020), 1–36.Google ScholarGoogle Scholar
  20. D. Giannone, L. Reichlin, and D. Small. 2008. Nowcasting: The Real-Time Informational Content of Macroeconomic Data. Journal of Monetary Economics 55, 4 (2008), 665–676.Google ScholarGoogle ScholarCross RefCross Ref
  21. P. Goldsmith-Pinkham and A. Sojourner. 2020. Predicting Initial Unemployment Insurance Claims Using Google Trends. Technical Report. Yale School of Management. https://paulgp.github.io/GoogleTrendsUINowcast/google_trends_UI.htmlGoogle ScholarGoogle Scholar
  22. J. W. Goodell. 2020. COVID-19 and finance: Agendas for future research. Finance Research Letters 35 (2020), 101512.Google ScholarGoogle ScholarCross RefCross Ref
  23. I. Goodfellow, Y. Bengio, and A. Courville. 2016. Deep Learning. MIT Press, US.Google ScholarGoogle Scholar
  24. C. Gormley and Z. Tong. 2015. Elasticsearch: The definitive guide. O’ Reilly Media, United States.Google ScholarGoogle Scholar
  25. T. B. Götz and T. A. Knetsch. 2019. Google data in bridge equation models for German GDP. International Journal of Forecasting 35, 1 (2019), 45–66.Google ScholarGoogle ScholarCross RefCross Ref
  26. A. Hamid and M. Heiden. 2015. Forecasting volatility with empirical similarity and Google Trends. Journal of Economic Behavior & Organization 117 (2015), 62–81.Google ScholarGoogle ScholarCross RefCross Ref
  27. G. Koop and L. Onorante. 2019. Macroeconomic Nowcasting Using Google Probabilities. Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part A (Advances in Econometrics) 40(2019), 17–40.Google ScholarGoogle Scholar
  28. M.J. Kusner, Y. Sun, N.I. Kolkin, and K.Q. Weinberger. 2015. From word embeddings to document distances. In 32nd International Conference on Machine Learning (ICML’15), Vol. 2. ACM, United States, 957–966.Google ScholarGoogle Scholar
  29. V. Kuzin, M. Marcellino, and C. Schumacher. 2011. MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area. International Journal of Forecasting 27, 2 (2011), 529–542.Google ScholarGoogle ScholarCross RefCross Ref
  30. H. Kwak and J. An. 2014. A First Look at Global News Coverage of Disasters by Using the GDELT Dataset. Springer International Publishing, Cham, 300–308.Google ScholarGoogle Scholar
  31. W. D. Larson and T. M. Sinclair. 2021. Nowcasting unemployment insurance claims in the time of COVID-19. International Journal of Forecasting 38, 2 (2021), 635–647.Google ScholarGoogle ScholarCross RefCross Ref
  32. Y. LeCun, Y. Bengio, and G. Hinton. 2015. Deep Learning. Nature 521, 7553 (2015), 436–444.Google ScholarGoogle Scholar
  33. K. Leetaru and P. A. Schrodt. 2013. GDELT: Global Data on Events, Location and Tone. Technical Report. KOF Working Papers, 1979-2012.Google ScholarGoogle Scholar
  34. T. Marwala. 2013. Economic modeling using Artificial Intelligence methods. Springer, Switzerland.Google ScholarGoogle Scholar
  35. V. Marx. 2013. The Big Challenges of Big Data. Nature 498(2013), 255–260.Google ScholarGoogle ScholarCross RefCross Ref
  36. T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems (NIPS 2013). ACM, United States, 3111–3119.Google ScholarGoogle Scholar
  37. J. Pennington, R. Socher, and C.D. Manning. 2014. GloVe: Global vectors for word representation. In EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference. ACL, United States, 1532–1543.Google ScholarGoogle Scholar
  38. N. Shah, D. Willick, and V. Mago. 2022. A framework for social media data analytics using Elasticsearch and Kibana. Wireless Networks 28(2022), 1179–1187.Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. B. Siliverstovs and D. S. Wochner. 2018. Google Trends and reality: Do the proportions match?: Appraising the informational value of online search behavior: Evidence from Swiss tourism regions. Journal of Economic Behavior & Organization 145 (2018), 1–23.Google ScholarGoogle ScholarCross RefCross Ref
  40. M. Taddy. 2019. Business Data Science: Combining Machine Learning and Economics to optimize, automate, and accelerate business decisions. McGraw-Hill, United States.Google ScholarGoogle Scholar
  41. Alberti V, Caperna G, Colagrossi M, Geraci A, Mazzarella G, Panella F, and Saisana M. 2021. Tracking EU Citizens? Interest in EC Priorities Using Online Search Data - The European Green Deal. Publications Office of the European Union, Luxembourg (Luxembourg). https://doi.org/10.2760/18216 (online)Google ScholarGoogle Scholar
  42. S. Vosen and T. Schmidt. 2011. Forecasting private consumption: survey-based indicators vs. Google trends. Journal of Forecasting 30, 6 (2011), 565–578.Google ScholarGoogle ScholarCross RefCross Ref
  43. S. Vosen and T. Schmidt. 2012. A monthly consumption indicator for Germany based on Internet search query data. Applied Economics Letters 19, 7 (2012), 683–687.Google ScholarGoogle ScholarCross RefCross Ref
  44. I. Wilms, S. Basu, J. Bien, and D. S. Matteson. 2021. Sparse identification and estimation of large-scale vector autoregressive moving averages. J. Amer. Statist. Assoc. (in press) (2021), 1–12. https://doi.org/10.1080/01621459.2021.1942013Google ScholarGoogle Scholar
  45. D. Zhang, M. Hu, and Q. Ji. 2020. Financial markets under the global pandemic of COVID-19. Finance Research Letters 36 (2020), 101528.Google ScholarGoogle ScholarCross RefCross Ref
  46. S. Zheng, J. Wu, M. E. Kahn, and Y. Deng. 2012. The nascent market for “green” real estate in Beijing. European Economic Review 56, 5 (2012), 974–984.Google ScholarGoogle ScholarCross RefCross Ref

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              cover image ACM Conferences
              GoodIT '22: Proceedings of the 2022 ACM Conference on Information Technology for Social Good
              September 2022
              436 pages
              ISBN:9781450392846
              DOI:10.1145/3524458

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              • Published: 7 September 2022

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