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Measuring and forecasting retail trade in real time using card transactional data

Author

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  • García, Juan R.
  • Pacce, Matías
  • Rodrigo, Tomasa
  • Ruiz de Aguirre, Pep
  • Ulloa, Camilo A.

Abstract

We build big data retail trade indicators for Spain using high-dimensional card transaction data from one of the country’s biggest banks. The resulting indicators replicate the dynamics of the Spanish retail trade indices (RTI), regional RTIs (Spain’s autonomous regions), and RTI by retailer type (distribution classes) released by the Spanish National Statistics Institute. The new indicators not only have a higher frequency (daily data) and higher geographical and sectorial breakdown but are also shown to improve nowcasting and forecasting power for the official RTI, making them key variables to monitor consumption.

Suggested Citation

  • García, Juan R. & Pacce, Matías & Rodrigo, Tomasa & Ruiz de Aguirre, Pep & Ulloa, Camilo A., 2021. "Measuring and forecasting retail trade in real time using card transactional data," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1235-1246.
    • Handle: RePEc:eee:intfor:v:37:y:2021:i:3:p:1235-1246
    DOI: 10.1016/j.ijforecast.2021.02.005
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    as
    1. D’Amuri, Francesco & Marcucci, Juri, 2017. "The predictive power of Google searches in forecasting US unemployment," International Journal of Forecasting, Elsevier, vol. 33(4), pages 801-816.
    2. Harvey, Andrew & Koopman, Siem Jan & Riani, Marco, 1997. "The Modeling and Seasonal Adjustment of Weekly Observations," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(3), pages 354-368, July.
    3. 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.
    4. Sean J. Taylor & Benjamin Letham, 2018. "Forecasting at Scale," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 37-45, January.
    5. 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.
    6. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. Galbraith, John W. & Tkacz, Greg, 2018. "Nowcasting with payments system data," International Journal of Forecasting, Elsevier, vol. 34(2), pages 366-376.
    13. Esselink, Henk & Gijsel, Lola Hernandez-van, 2017. "The use of cash by households in the euro area," Occasional Paper Series 201, European Central Bank.
    14. 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.
    15. 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.
    16. 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.
    17. Stephens Melvin, 2006. "Paycheque Receipt and the Timing of Consumption," Economic Journal, Royal Economic Society, vol. 116(513), pages 680-701, July.
    18. 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.
    Full references (including those not matched with items on IDEAS)

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    1. Rodríguez Mora, José V & Buda, Gergely & Carvalho, Vasco & Hansen, Stephen & Ortiz, Alvaro & Rodrigo, Tomasa, 2022. "National Accounts in a World of Naturally Occurring Data: A Proof of Concept for Consumption," CEPR Discussion Papers 17519, C.E.P.R. Discussion Papers.
    2. Juan D. Borrero & Jesus Mariscal, 2021. "Deterministic Chaos Detection and Simplicial Local Predictions Applied to Strawberry Production Time Series," Mathematics, MDPI, vol. 9(23), pages 1-18, November.
    3. Ludmila Fadejeva & Boriss Siliverstovs & Karlis Vilerts & Anete Brinke, 2022. "Consumer Spending in the Covid-19 Pandemic: Evidence from Card Transactions in Latvia," Discussion Papers 2022/01, Latvijas Banka.
    4. 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.
    5. Anete Brinke & Ludmila Fadejeva & Boriss Siliverstovs & Kārlis Vilerts, 2023. "Assessing the informational content of card transactions for nowcasting retail trade: Evidence for Latvia," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 566-577, April.

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