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Big data financial transactions and GDP nowcasting: The case of Turkey

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  • Ali B. Barlas
  • Seda Guler Mert
  • Berk Orkun Isa
  • Alvaro Ortiz
  • Tomasa Rodrigo
  • Baris Soybilgen
  • Ege Yazgan

Abstract

We use aggregated information from individual‐to‐firm and firm‐to‐firm transactions from the Garanti BBVA Bank to simulate domestic private demand and estimate aggregate consumption and investment for Turkey's quarterly national accounts in real time. We show that these big data variables successfully nowcast official consumption and investment flows. To further validate the usefulness of these indicators, we include both indicators among others which are generally used in gross domestic product (GDP) nowcasting and evaluate their contribution to nowcasting power of Turkish GDP by combining both linear and nonlinear models. The results are successful and confirm the usefulness of consumption and investment banking transactions for nowcasting purposes. These big data are valuable, especially at the beginning of the nowcasting process, when the traditional hard data are scarce. Accordingly, this information is especially relevant for countries with longer statistical release lags, such as emerging markets.

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  • Ali B. Barlas & Seda Guler Mert & Berk Orkun Isa & Alvaro Ortiz & Tomasa Rodrigo & Baris Soybilgen & Ege Yazgan, 2024. "Big data financial transactions and GDP nowcasting: The case of Turkey," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 227-248, March.
    • Handle: RePEc:wly:jforec:v:43:y:2024:i:2:p:227-248
    DOI: 10.1002/for.3032
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    as
    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    2. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    3. 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.
    4. 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).
    5. Timmermann, Allan, 2006. "Forecast Combinations," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 4, pages 135-196, Elsevier.
    6. Capistrán, Carlos & Timmermann, Allan, 2009. "Forecast Combination With Entry and Exit of Experts," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 428-440.
    7. 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.
    8. Edika G. Quispe-Torreblanca & Neil Stewart & John Gathergood & George Loewenstein, 2019. "The Red, the Black, and the Plastic: Paying Down Credit Card Debt for Hotels, Not Sofas," Management Science, INFORMS, vol. 65(11), pages 5392-5410, November.
    9. Nicolas Woloszko, 2020. "Tracking activity in real time with Google Trends," OECD Economics Department Working Papers 1634, OECD Publishing.
    10. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    11. Oriol Aspachs & Ruben Durante & Alberto Graziano & Josep Mestres & Jose G Montalvo & Marta Reynal-Querol, 2022. "Real-time inequality and the welfare state in motion: evidence from COVID-19 in Spain," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 37(109), pages 165-199.
    12. 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.
    13. Valentina Aprigliano, 2020. "A large Bayesian VAR with a block‐specific shrinkage: A forecasting application for Italian industrial production," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1291-1304, December.
    14. Natalie Cox & Peter Ganong & Pascal Noel & Joseph Vavra & Arlene Wong & Diana Farrell & Fiona Greig & Erica Deadman, 2020. "Initial Impacts of the Pandemic on Consumer Behavior: Evidence from Linked Income, Spending, and Savings Data," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 51(2 (Summer), pages 35-82.
    15. Domenico Giannone & Lucrezia Reichlin & Saverio Simonelli, 2009. "Nowcasting Euro Area Economic Activity In Real Time: The Role Of Confidence Indicators," National Institute Economic Review, National Institute of Economic and Social Research, vol. 210(1), pages 90-97, October.
    16. Valentina Aprigliano & Guerino Ardizzi & Libero Monteforte, 2019. "Using Payment System Data to Forecast Economic Activity," International Journal of Central Banking, International Journal of Central Banking, vol. 15(4), pages 55-80, October.
    17. Scott R. Baker & Robert A Farrokhnia & Steffen Meyer & Michaela Pagel & Constantine Yannelis, 2023. "Income, Liquidity, and the Consumption Response to the 2020 Economic Stimulus Payments," Review of Finance, European Finance Association, vol. 27(6), pages 2271-2304.
    18. 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.
    19. Modugno, Michele & Soybilgen, Barış & Yazgan, Ege, 2016. "Nowcasting Turkish GDP and news decomposition," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1369-1384.
    20. 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.
    21. Lorenz Kueng & Scott R. Baker, 2022. "Household Financial Transaction Data," Annual Review of Economics, Annual Reviews, vol. 14(1), pages 47-67, August.
    22. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    23. 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.
    24. David F. Hendry & Michael P. Clements, 2004. "Pooling of forecasts," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 1-31, June.
    25. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    26. Roy Verbaan & Wilko Bolt & Carin van der Cruijsen, 2017. "Using debit card payments data for nowcasting Dutch household consumption," DNB Working Papers 571, Netherlands Central Bank, Research Department.
    27. 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.
    28. 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.
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