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Tracking the future on the web: construction of leading indicators using internet searches


  • Concha Artola

    () (Banco de España)

  • Enrique Galán

    () (Banco de España)


This paper reviews some of the applications that use the vast swathes of information provided by Internet user searches for economic analysis and forecasting. This enormous volume of information, available in real time, can be handled by analysts thanks to statistical tools such as “Google Insights for Search”, which allow trends in different areas of interest to be classified and evaluated. Previous work focused predominantly on the labour market, on the housing market, on retail sales and on consumer confidence. This paper presents a very specific application for the Spanish economy: British tourist inflows to Spain (the Spanish tourist industry's main customers). The improvement in the forecasting provided by the short-term models that include the G-indicator depends on the benchmark model. This does, however, allow an adjusted indicator of the inflow of British tourists to be obtained with a lead of almost one month. This is but an initial step in the use of on-line searches for constructing leading indicators of economic activity. Other applications to be explored are car sales, consumer confidence and house purchases. The chief characteristic of these procedures is that, with time and the continuous growth of Internet use, results can only improve in the future. It should nonetheless be recalled that the construction of these G-indicators requires caution so as to avoid mistakes arising, inter alia, from the different use of language in different countries. Not taking due caution and blindly confiding in these indicators may lead to erroneous results being obtained.

Suggested Citation

  • Concha Artola & Enrique Galán, 2012. "Tracking the future on the web: construction of leading indicators using internet searches," Occasional Papers 1203, Banco de España;Occasional Papers Homepage.
  • Handle: RePEc:bde:opaper:1203

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    Cited by:

    1. Vicente, María Rosalía & López-Menéndez, Ana J. & Pérez, Rigoberto, 2015. "Forecasting unemployment with internet search data: Does it help to improve predictions when job destruction is skyrocketing?," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 132-139.
    2. repec:eee:intfor:v:33:y:2017:i:4:p:801-816 is not listed on IDEAS
    3. Steven L. Scott & Hal R. Varian, 2015. "Bayesian Variable Selection for Nowcasting Economic Time Series," NBER Chapters,in: Economic Analysis of the Digital Economy, pages 119-135 National Bureau of Economic Research, Inc.
    4. Azusa Matsumoto & Kohei Matsumura & Noriyuki Shiraki, 2013. "Potential of Search Data in Assessment of Current Economic Conditions," Bank of Japan Research Papers 2013-04-18, Bank of Japan.

    More about this item


    keyword; Google; forecasting; nowcasting; tourism;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

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