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Measuring retail trade using card transactional data

Author

Listed:
  • Diego Bodas

    (Mapfre)

  • Juan R. García López

    (BBVA Research)

  • Tomasa Rodrigo López

    (BBVA Research)

  • Pep Ruiz de Aguirre

    (BBVA Research)

  • Camilo A. Ulloa

    (BBVA Research)

  • Juan Murillo Arias

    (BBVA data & analytics)

  • Juan de Dios Romero Palop

    (BBVA data & analytics)

  • Heribert Valero Lapaz

    (BBVA data & analytics)

  • Matías J. Pacce

    (Banco de España)

Abstract

In this paper we present a high-dimensionality Retail Trade Index (RTI) constructed to nowcast the retail trade sector economic performance in Spain, using Big Data sources and techniques. The data are the footprints of BBVA clients from their credit or debit card transactions at Spanish point of sale (PoS) terminals. The resulting indexes have been found to be robust when compared with the Spanish RTI, regional RTI (Spain’s autonomous regions), and RTI by retailer type (distribution classes) published by the National Statistics Institute (INE). We also went one step further, computing the monthly indexes for the provinces and sectors of activity and the daily general index, by obtaining timely, detailed information on retail sales. Finally, we analyzed the high-frequency consumption dynamics using BBVA retailer behavior and a structural time series model.

Suggested Citation

  • 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.
    • Handle: RePEc:bde:wpaper:1921
    as

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    File URL: https://www.bde.es/f/webbde/SES/Secciones/Publicaciones/PublicacionesSeriadas/DocumentosTrabajo/19/Fich/dt1921e.pdf
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    References listed on IDEAS

    as
    1. Melvin Stephens Jr., 2003. ""3rd of tha Month": Do Social Security Recipients Smooth Consumption Between Checks?," American Economic Review, American Economic Association, vol. 93(1), pages 406-422, March.
    2. Emma Aguila & Arie Kapteyn & Francisco Perez-Arce, 2017. "Consumption Smoothing and Frequency of Benefit Payments of Cash Transfer Programs," American Economic Review, American Economic Association, vol. 107(5), pages 430-435, May.
    3. 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.
    4. Bell, William R & Hillmer, Steven C, 1984. "Issues Involved with the Seasonal Adjustment of Time Series: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 343-349, October.
    5. Alberto Cabrero & Gonzalo Camba-Mendez & Astrid Hirsch & Fernando Nieto, 2009. "Modelling the daily banknotes in circulation in the context of the liquidity management of the European Central Bank," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(3), pages 194-217.
    6. Giovanni Mastrobuoni & Matthew Weinberg, 2009. "Heterogeneity in Intra-monthly Consumption Patterns, Self-Control, and Savings at Retirement," American Economic Journal: Economic Policy, American Economic Association, vol. 1(2), pages 163-189, August.
    7. Bell, William R & Hillmer, Steven C, 1984. "Issues Involved with the Seasonal Adjustment of Economic Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 291-320, October.
    8. Shapiro, Jesse M., 2005. "Is there a daily discount rate? Evidence from the food stamp nutrition cycle," Journal of Public Economics, Elsevier, vol. 89(2-3), pages 303-325, February.
    9. McLaren, Nick & Shanbhogue, Rachana, 2011. "Using internet search data as economic indicators," Bank of England Quarterly Bulletin, Bank of England, vol. 51(2), pages 134-140.
    10. María Gil & Javier J. Pérez & Alberto Urtasun, 2019. "Nowcasting private consumption: traditional indicators, uncertainty measures, credit cards and some internet data," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The use of big data analytics and artificial intelligence in central banking, volume 50, Bank for International Settlements.
    11. Maximo Camacho & Matias Pacce, 2016. "Forecasting travelers in Spain with Google queries," Working Papers 16/20, BBVA Bank, Economic Research Department.
    12. James H. Stock & Mark W. Watson, 1989. "New Indexes of Coincident and Leading Economic Indicators," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 351-409, National Bureau of Economic Research, Inc.
    13. Maximo Camacho & Matías José Pacce, 2018. "Forecasting travellers in Spain with Google’s search volume indices," Tourism Economics, , vol. 24(4), pages 434-448, June.
    14. Galbraith, John W. & Tkacz, Greg, 2015. "Nowcasting GDP with electronic payments data," Statistics Paper Series 10, European Central Bank.
    15. Alberto Cavallo & Roberto Rigobon, 2016. "The Billion Prices Project: Using Online Prices for Measurement and Research," Journal of Economic Perspectives, American Economic Association, vol. 30(2), pages 151-178, Spring.
    16. Stephens Melvin, 2006. "Paycheque Receipt and the Timing of Consumption," Economic Journal, Royal Economic Society, vol. 116(513), pages 680-701, July.
    17. 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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    Cited by:

    1. Juan de Dios Romero Palop & Juan Murillo Arias & Diego J. Bodas-Sagi & Heribert Valero Lapaz, 2019. "Determining the usual environment of cardholders as a key factor to measure the evolution of domestic tourism," Information Technology & Tourism, Springer, vol. 21(1), pages 23-43, March.
    2. Carvalho, V & Garcia, Juan R. & Hansen, S. & Ortiz, A. & Rodrigo, T. & More, J. V. R., 2020. "Tracking the COVID-19 Crisis with High-Resolution Transaction Data," Cambridge Working Papers in Economics 2030, Faculty of Economics, University of Cambridge.
    3. 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.
    4. María Gil & Javier J. Pérez & Alberto Urtasun, 2019. "Nowcasting private consumption: traditional indicators, uncertainty measures, credit cards and some internet data," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The use of big data analytics and artificial intelligence in central banking, volume 50, Bank for International Settlements.
    5. Valentina Aprigliano & Guerino Ardizzi & Alessia Cassetta & Alessandro Cavallero & Simone Emiliozzi & Alessandro Gambini & Nazzareno Renzi & Roberta Zizza, 2021. "Exploiting payments to track Italian economic activity: the experience at Banca d’Italia," Questioni di Economia e Finanza (Occasional Papers) 609, Bank of Italy, Economic Research and International Relations Area.

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    More about this item

    Keywords

    retail sales; big data; electronic payments; consumption; structural time series model;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth

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