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Big Data Information and Nowcasting: Consumption and Investment from Bank Transactions in Turkey

Author

Listed:
  • Ali B. Barlas

    (BBVA Research)

  • Seda Guler Mert

    (BBVA Research)

  • Berk Orkun Isa

    (BBVA Research)

  • Alvaro Ortiz

    (BBVA Research)

  • Tomasa Rodrigo

    (BBVA Research)

  • Baris Soybilgen

    (Bilgi University)

  • Ege Yazgan

    (Bilgi University)

Abstract

We use the aggregate information from individual-to-firm and firm-to-firm in Garanti BBVA Bank transactions to mimic domestic private demand. Particularly, we replicate the quarterly national accounts aggregate consumption and investment (gross fixed capital formation) and its bigger components (Machinery and Equipment and Construction) in real time for the case of Turkey. In order to validate the usefulness of the information derived from these indicators we test the nowcasting ability of both indicators to nowcast the Turkish GDP using different nowcasting models. The results are successful and confirm the usefulness of Consumption and Investment Banking transactions for nowcasting purposes. The value of the Big data information is more relevant at the beginning of the nowcasting process, when the traditional hard data information is scarce. This makes this information specially relevant for those countries where statistical release lags are longer like the Emerging Markets.

Suggested Citation

  • Ali B. Barlas & Seda Guler Mert & Berk Orkun Isa & Alvaro Ortiz & Tomasa Rodrigo & Baris Soybilgen & Ege Yazgan, 2021. "Big Data Information and Nowcasting: Consumption and Investment from Bank Transactions in Turkey," Papers 2107.03299, arXiv.org.
    • Handle: RePEc:arx:papers:2107.03299
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    References listed on IDEAS

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    1. Surico, Paolo & Känzig, Diego & Hacıoğlu Hoke, Sinem, 2020. "Consumption in the time of Covid-19: Evidence from UK transaction data," CEPR Discussion Papers 14733, C.E.P.R. Discussion Papers.
    2. Galbraith, John W. & Tkacz, Greg, 2015. "Nowcasting GDP with electronic payments data," Statistics Paper Series 10, European Central Bank.
    3. Barnett, William & Chauvet, Marcelle & Leiva-Leon, Danilo & Su, Liting, 2016. "Nowcasting Nominal GDP with the Credit-Card Augmented Divisia Monetary," Studies in Applied Economics 59, The Johns Hopkins Institute for Applied Economics, Global Health, and the Study of Business Enterprise.
    4. Nicolas Woloszko, 2020. "Tracking activity in real time with Google Trends," OECD Economics Department Working Papers 1634, OECD Publishing.
    5. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2022. "Machine Learning Time Series Regressions With an Application to Nowcasting," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1094-1106, June.
    6. Huiyu Huang & Tae-Hwy Lee, 2010. "To Combine Forecasts or to Combine Information?," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 534-570.
    7. Asger Lau Andersen & Emil Toft Hansen & Niels Johannesen & Adam Sheridan, 2022. "Consumer responses to the COVID‐19 crisis: evidence from bank account transaction data," Scandinavian Journal of Economics, Wiley Blackwell, vol. 124(4), pages 905-929, October.
    8. Arna Olafsson & Michaela Pagel, 2018. "The Liquid Hand-to-Mouth: Evidence from Personal Finance Management Software," The Review of Financial Studies, Society for Financial Studies, vol. 31(11), pages 4398-4446.
    9. Frank Schorfheide & Dongho Song, 2015. "Real-Time Forecasting With a Mixed-Frequency VAR," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 366-380, July.
    10. Hansen, Stephen & Carvalho, Vasco & García, Juan Ramón & Ortiz, Alvaro & Rodrigo, Tomasa & Rodríguez Mora, José V & Ruiz, Pep, 2020. "Tracking the COVID-19 Crisis with High-Resolution Transaction Data," CEPR Discussion Papers 14642, C.E.P.R. Discussion Papers.
    11. Soybilgen, Barış & Yazgan, Ege, 2018. "Evaluating nowcasts of bridge equations with advanced combination schemes for the Turkish unemployment rate," Economic Modelling, Elsevier, vol. 72(C), pages 99-108.
    12. Akcigit, Ufuk & Akgunduz, Yusuf Emre & Cilasun, Seyit Mumin & Ozcan-Tok, Elif & Yilmaz, Fatih, 2020. "Facts on business dynamism in Turkey," European Economic Review, Elsevier, vol. 128(C).
    13. David Bounie & Youssouf Camara & John Galbraith, 2020. "Consumers’ Mobility, Expenditure and Online-Offline Substitution Response to COVID-19: Evidence from French Transaction Data," Cahiers de recherche 14-2020, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    14. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    15. David F. Hendry & Michael P. Clements, 2004. "Pooling of forecasts," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 1-31, June.
    16. Modugno, Michele & Soybilgen, Barış & Yazgan, Ege, 2016. "Nowcasting Turkish GDP and news decomposition," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1369-1384.
    17. Ferhan Gezici & Burçin Yazgı Walsh & Sinem Metin Kacar, 2017. "Regional and structural analysis of the manufacturing industry in Turkey," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 59(1), pages 209-230, July.
    18. Valentina Aprigliano & Guerino Ardizzi & Libero Monteforte, 2017. "Using the payment system data to forecast the Italian GDP," Temi di discussione (Economic working papers) 1098, Bank of Italy, Economic Research and International Relations Area.
    19. António Rua & Nuno Lourenço, 2020. "The DEI: tracking economic activity daily during the lockdown," Working Papers w202013, Banco de Portugal, Economics and Research Department.
    20. Diego Bodas & Juan R. García López & Tomasa Rodrigo López & Pep Ruiz de Aguirre & Camilo A. Ulloa & Juan Murillo Arias & Juan de Dios Romero Palop & Heribert Valero Lapaz & Matías J. Pacce, 2019. "Measuring retail trade using card transactional data," Working Papers 1921, Banco de España.
    21. Marta Bańbura & Michele Modugno, 2014. "Maximum Likelihood Estimation Of Factor Models On Datasets With Arbitrary Pattern Of Missing Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(1), pages 133-160, January.
    22. Daniel J. Lewis & Karel Mertens & James H. Stock & Mihir Trivedi, 2022. "Measuring real activity using a weekly economic index," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 667-687, June.
    23. Ankargren Sebastian & Unosson Måns & Yang Yukai, 2020. "A Flexible Mixed-Frequency Vector Autoregression with a Steady-State Prior," Journal of Time Series Econometrics, De Gruyter, vol. 12(2), pages 1-41, July.
    24. Diane Alexander & Ezra Karger, 2023. "Do Stay-at-Home Orders Cause People to Stay at Home? Effects of Stay-at-Home Orders on Consumer Behavior," The Review of Economics and Statistics, MIT Press, vol. 105(4), pages 1017-1027, July.
    25. Genre, Véronique & Kenny, Geoff & Meyler, Aidan & Timmermann, Allan, 2013. "Combining expert forecasts: Can anything beat the simple average?," International Journal of Forecasting, Elsevier, vol. 29(1), pages 108-121.
    26. Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
    27. Duarte, Cláudia & Rodrigues, Paulo M.M. & Rua, António, 2017. "A mixed frequency approach to the forecasting of private consumption with ATM/POS data," International Journal of Forecasting, Elsevier, vol. 33(1), pages 61-75.
    28. Barış Soybilgen & Ege Yazgan, 2021. "Nowcasting US GDP Using Tree-Based Ensemble Models and Dynamic Factors," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 387-417, January.
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    1. Tomas Adam & Jan Belka & Martin Hluze & Jakub Mateju & Hana Prause & Jiri Schwarz, 2023. "Ace in Hand: The Value of Card Data in the Game of Nowcasting," Working Papers 2023/14, Czech National Bank.
    2. Simone Emiliozzi & Concetta Rondinelli & Stefania Villa, 2023. "Consumption during the Covid-19 pandemic: evidence from Italian credit cards," Questioni di Economia e Finanza (Occasional Papers) 769, Bank of Italy, Economic Research and International Relations Area.

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