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Nowcasting private consumption: traditional indicators, uncertainty measures, credit cards and some internet data


  • María Gil

    (Banco de España)

  • Javier J. Pérez

    (Banco de España)

  • A. Jesús Sánchez

    (Instituto Complutense de Estudios Internacionales (UCM) and GEN)

  • Alberto Urtasun

    (Banco de España)


The focus of this paper is on nowcasting and forecasting quarterly private consumption. The selection of real-time, monthly indicators focuses on standard (“hard” / “soft” indicators) and less-standard variables. Among the latter group we analyze: i) proxy indicators of economic and policy uncertainty; ii) payment cards’ transactions, as measured at “Point-of-sale” (POS) and ATM withdrawals; iii) indicators based on consumption-related search queries retrieved by means of the Google Trends application. We estimate a suite of mixed-frequency, time series models at the monthly frequency, on a real-time database with Spanish data, and conduct out-of-sample forecasting exercises to assess the relevant merits of the different groups of indicators. Some results stand out: i) “hard” and payments cards indicators are the best performers when taken individually, and more so when combined; ii) nonetheless, “soft” indicators are helpful to detect qualitative signals in the nowcasting horizon; iii) Google-based and uncertainty indicators add value when combined with traditional indicators, most notably at estimation horizons beyond the nowcasting one, what would be consistent with capturing information about future consumption decisions; iv) the combinations of models that include the best performing indicators tend to beat broader-based combinations.

Suggested Citation

  • María Gil & Javier J. Pérez & A. Jesús Sánchez & Alberto Urtasun, 2018. "Nowcasting private consumption: traditional indicators, uncertainty measures, credit cards and some internet data," Working Papers 1842, Banco de España.
  • Handle: RePEc:bde:wpaper:1842

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    References listed on IDEAS

    1. Stefano Grassi & Tommaso Proietti & Cecilia Frale & Massimiliano Marcellino & Gianluigi Mazzi, 2014. "EuroMInd-C: a Disaggregate Monthly Indicator of Economic Activity for the Euro," Studies in Economics 1406, School of Economics, University of Kent.
    2. Konstantin A. Kholodilin & Maximilian Podstawski & Boriss Siliverstovs, 2010. "Do Google Searches Help in Nowcasting Private Consumption?: A Real-Time Evidence for the US," Discussion Papers of DIW Berlin 997, DIW Berlin, German Institute for Economic Research.
    3. Fondeur, Y. & Karamé, F., 2013. "Can Google data help predict French youth unemployment?," Economic Modelling, Elsevier, vol. 30(C), pages 117-125.
    4. R?diger Bachmann & Steffen Elstner & Eric R. Sims, 2013. "Uncertainty and Economic Activity: Evidence from Business Survey Data," American Economic Journal: Macroeconomics, American Economic Association, vol. 5(2), pages 217-249, April.
    5. Clark, Todd E. & McCracken, Michael W., 2001. "Tests of equal forecast accuracy and encompassing for nested models," Journal of Econometrics, Elsevier, vol. 105(1), pages 85-110, November.
    6. Caggiano, Giovanni & Castelnuovo, Efrem & Groshenny, Nicolas, 2014. "Uncertainty shocks and unemployment dynamics in U.S. recessions," Journal of Monetary Economics, Elsevier, vol. 67(C), pages 78-92.
    7. M. E. Bontempi & R. Golinelli & M. Squadrani, 2016. "A New Index of Uncertainty Based on Internet Searches: A Friend or Foe of Other Indicators?," Working Papers wp1062, Dipartimento Scienze Economiche, Universita' di Bologna.
    8. Narcissa Balta & Ismael Valdes Fernandez & Eric Ruscher, 2013. "Assessing the impact of uncertainty on consumption and investment," Quarterly Report on the Euro Area (QREA), Directorate General Economic and Financial Affairs (DG ECFIN), European Commission, vol. 12(2), pages 7-16, June.
    9. Pesaran, M Hashem & Timmermann, Allan, 1992. "A Simple Nonparametric Test of Predictive Performance," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(4), pages 561-565, October.
    10. Adina Popescu & Frank Rafael Smets, 2010. "Uncertainty, Risk-taking, and the Business Cycle in Germany," CESifo Economic Studies, CESifo, vol. 56(4), pages 596-626, December.
    11. Konstantin Kholodilin & Maximilian Podstawski & Boriss Siliverstovs, 2010. "Do Google Searches Help in Nowcasting Private Consumption?," KOF Working papers 10-256, KOF Swiss Economic Institute, ETH Zurich.
    12. María Gil & Javier J. Pérez & Alberto Urtasun, 2017. "Macroeconomic uncertainty: measurement and impact on the Spanish economy," Economic Bulletin, Banco de España, issue MAR, pages 1-13.
    13. Diego Bodas & Juan Ramon Garcia & Juan Murillo & Matias Pacce & Tomasa Rodrigo & Juan de Dios Romero & Pep Ruiz & Camilo Ulloa & Heribert Valero, 2018. "Measuring Retail Trade Using Card Transactional Data," Working Papers 18/03, BBVA Bank, Economic Research Department.
    14. Chow, Gregory C & Lin, An-loh, 1971. "Best Linear Unbiased Interpolation, Distribution, and Extrapolation of Time Series by Related Series," The Review of Economics and Statistics, MIT Press, vol. 53(4), pages 372-375, November.
    15. Filippo Moauro, 2014. "Monthly Employment Indicators of the Euro Area and Larger Member States: Real‐Time Analysis of Indirect Estimates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(5), pages 339-349, August.
    16. 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.
    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.
    18. Paul Smith, 2016. "Google's MIDAS Touch: Predicting UK Unemployment with Internet Search Data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(3), pages 263-284, April.
    19. Schmidt, Torsten & Vosen, Simeon, 2012. "Using Internet Data to Account for Special Events in Economic Forecasting," Ruhr Economic Papers 382, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
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    Cited by:

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    2. Nuttanan Wichitaksorn, 2020. "Analyzing and Forecasting Thai Macroeconomic Data using Mixed-Frequency Approach," PIER Discussion Papers 146, Puey Ungphakorn Institute for Economic Research.
    3. Joaquín Artés & Ana Melissa Botello Mainieri & A. Jesús Sánchez-Fuentes, 2019. "Tax reforms and Google searches: the case of Spanish VAT reforms during the great recession," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 10(3), pages 321-336, November.
    4. Maria Begicheva & Alexey Zaytsev, 2021. "Bank transactions embeddings help to uncover current macroeconomics," Papers 2110.12000,, revised Dec 2021.
    5. 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.

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    More about this item


    private consumption; nowcasting; forecasting; uncertainty; Google Trends.;
    All these keywords.

    JEL classification:

    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • 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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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