Tracking the future on the web: construction of leading indicators using internet searches
AbstractThis 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.
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Bibliographic InfoPaper provided by Banco de Espa�a in its series Banco de Espa�a Occasional Papers with number 1203.
Length: 29 pages
Date of creation: Apr 2012
Date of revision:
Publication status: Published also in Spanish, with the same number.
keyword; Google; forecasting; nowcasting; tourism;
Find related papers by JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull 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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- Steven L. Scott & Hal Varian, 2013.
"Bayesian Variable Selection for Nowcasting Economic Time Series,"
in: Economics of Digitization
National Bureau of Economic Research, Inc.
- Steven L. Scott & Hal R. Varian, 2013. "Bayesian Variable Selection for Nowcasting Economic Time Series," NBER Working Papers 19567, National Bureau of Economic Research, Inc.
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