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Can Google econometrics predict unemployment? Evidence from Spain

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  • González-Fernández, Marcos
  • González-Velasco, Carmen

Abstract

The aim of the paper is to analyze the ability of internet activity, what has been called Google econometrics, to predict unemployment in Spain. We include a new predictor for Spanish unemployment based on internet information provided by Google Trends. Using monthly data from January 2004 to November 2017 we found evidence of a high correlation between internet queries and unemployment. Besides that, the inclusion of internet activity enhances model’s prediction performance.

Suggested Citation

  • González-Fernández, Marcos & González-Velasco, Carmen, 2018. "Can Google econometrics predict unemployment? Evidence from Spain," Economics Letters, Elsevier, vol. 170(C), pages 42-45.
    • Handle: RePEc:eee:ecolet:v:170:y:2018:i:c:p:42-45
    DOI: 10.1016/j.econlet.2018.05.031
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    1. Theologos Dergiades & Costas Milas & Theodore Panagiotidis, 2015. "Tweets, Google trends, and sovereign spreads in the GIIPS," Oxford Economic Papers, Oxford University Press, vol. 67(2), pages 406-432.
    2. Fondeur, Y. & Karamé, F., 2013. "Can Google data help predict French youth unemployment?," Economic Modelling, Elsevier, vol. 30(C), pages 117-125.
    3. Moussa, Faten & Delhoumi, Ezzeddine & Ouda, Olfa Ben, 2017. "Stock return and volatility reactions to information demand and supply," Research in International Business and Finance, Elsevier, vol. 39(PA), pages 54-67.
    4. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    5. Siganos, Antonios, 2013. "Google attention and target price run ups," International Review of Financial Analysis, Elsevier, vol. 29(C), pages 219-226.
    6. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    7. 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.
    8. Li, Xin & Ma, Jian & Wang, Shouyang & Zhang, Xun, 2015. "How does Google search affect trader positions and crude oil prices?," Economic Modelling, Elsevier, vol. 49(C), pages 162-171.
    9. Brad M. Barber & Terrance Odean, 2008. "All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors," Review of Financial Studies, Society for Financial Studies, vol. 21(2), pages 785-818, April.
    10. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    11. Matthias Bank & Martin Larch & Georg Peter, 2011. "Google search volume and its influence on liquidity and returns of German stocks," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 25(3), pages 239-264, September.
    12. Peri, Massimo & Vandone, Daniela & Baldi, Lucia, 2014. "Internet, noise trading and commodity futures prices," International Review of Economics & Finance, Elsevier, vol. 33(C), pages 82-89.
    13. McLaren, Nick & Shanbhogue, Rachana, 2011. "Using internet search data as economic indicators," Bank of England Quarterly Bulletin, Bank of England, vol. 51(2), pages 134-140.
    14. Vlastakis, Nikolaos & Markellos, Raphael N., 2012. "Information demand and stock market volatility," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1808-1821.
    15. Joseph, Kissan & Babajide Wintoki, M. & Zhang, Zelin, 2011. "Forecasting abnormal stock returns and trading volume using investor sentiment: Evidence from online search," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1116-1127, October.
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    Cited by:

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    2. Mendolia, Silvia & Stavrunova, Olena & Yerokhin, Oleg, 2021. "Determinants of the community mobility during the COVID-19 epidemic: The role of government regulations and information," Journal of Economic Behavior & Organization, Elsevier, vol. 184(C), pages 199-231.

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    More about this item

    Keywords

    Google econometrics; Unemployment; Spain; Forecasting;
    All these keywords.

    JEL classification:

    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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