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Nowcasting private consumption: traditional indicators, uncertainty measures, and the role of internet search query data

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  • Alberto Urtasun
  • Mara Gil
  • Javier J. Perez

Abstract

The aim of this paper is to nowcast quarterly private consumption in Spain. We estimate a suite of mixed-frequency models on a real-time database for the period The selection of indicators is guided by the standard practice (\hard" and \soft" indicators), but also expand this practice by looking at non-standard variables, namely: (i) a suite of proxy indicators of uncertainty, calculated at the monthly frequency; (ii) two additional sets of variables that are sampled at a much lower frequency: Credit card transactions and indicators based on search query time series provided by Google Trends. 2005Q1-2015Q4, and conduct out-of-sample forecasting exercises to assess the relevant merits of different groups of indicators. The selection of indicators is guided by the standard practice (\hard" and \soft" indicators), but also expand this practice by looking at non-standard variables, namely: (i) a suite of proxy indicators of uncertainty, calculated at the monthly frequency; (ii) two additional sets of variables that are sampled at a much lower frequency: Credit card transactions and indicators based on search query time series provided by Google Trends. The latter set of indicators is based on factors extracted from consumption-related search categories of the Google Trends application. We also illustrate how Google data (sampled at a frequency higher than monthly) can be instrumental to perform event studies, by looking at possible anticipation effects related to VAT increases.

Suggested Citation

  • Alberto Urtasun & Mara Gil & Javier J. Perez, 2017. "Nowcasting private consumption: traditional indicators, uncertainty measures, and the role of internet search query data," EcoMod2017 10745, EcoMod.
    • Handle: RePEc:ekd:010027:10745
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    References listed on IDEAS

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    1. repec:diw:diwwpp:dp997 is not listed on IDEAS
    2. Fondeur, Y. & Karamé, F., 2013. "Can Google data help predict French youth unemployment?," Economic Modelling, Elsevier, vol. 30(C), pages 117-125.
    3. Simeon Vosen & Torsten Schmidt, 2011. "Forecasting private consumption: survey‐based indicators vs. Google trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(6), pages 565-578, September.
    4. 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.
    5. Pedregal, Diego J. & Pérez, Javier J., 2010. "Should quarterly government finance statistics be used for fiscal surveillance in Europe?," International Journal of Forecasting, Elsevier, vol. 26(4), pages 794-807, October.
    6. 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.
    7. 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.
    8. 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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    More about this item

    Keywords

    Europe; Miscellaneous; Miscellaneous;
    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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