Tracking the future on the web: construction of leading indicators using internet searches
Author
Abstract
Suggested Citation
Download full text from publisher
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Dean Fantazzini & Julia Pushchelenko & Alexey Mironenkov & Alexey Kurbatskii, 2021.
"Forecasting Internal Migration in Russia Using Google Trends: Evidence from Moscow and Saint Petersburg,"
Forecasting, MDPI, vol. 3(4), pages 1-30, October.
- Fantazzini, Dean & Pushchelenko, Julia & Mironenkov, Alexey & Kurbatskii, Alexey, 2021. "Forecasting internal migration in Russia using Google Trends: Evidence from Moscow and Saint Petersburg," MPRA Paper 110452, University Library of Munich, Germany.
- Marcelo C. Medeiros & Henrique F. Pires, 2021. "The Proper Use of Google Trends in Forecasting Models," Papers 2104.03065, arXiv.org, revised Apr 2021.
- Konstantinos N. Konstantakis & Despoina Paraskeuopoulou & Panayotis G. Michaelides & Efthymios G. Tsionas, 2021. "Bank deposits and Google searches in a crisis economy: Bayesian non‐linear evidence for Greece (2009–2015)," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5408-5424, October.
- Marta Crispino & Vincenzo Mariani, 2023. "A tool to nowcast tourist overnight stays with payment data and complementary indicators," Questioni di Economia e Finanza (Occasional Papers) 746, Bank of Italy, Economic Research and International Relations Area.
- Javier Sebastian, 2016. "Blockchain in financial services: Regulatory landscape and future challenges," Working Papers 16/21, BBVA Bank, Economic Research Department.
- Nima Nonejad, 2021. "An Overview Of Dynamic Model Averaging Techniques In Time‐Series Econometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 566-614, April.
- D’Amuri, Francesco & Marcucci, Juri, 2017.
"The predictive power of Google searches in forecasting US unemployment,"
International Journal of Forecasting, Elsevier, vol. 33(4), pages 801-816.
- Francesco D'Amuri & Juri Marcucci, 2012. "The predictive power of Google searches in forecasting unemployment," Temi di discussione (Economic working papers) 891, Bank of Italy, Economic Research and International Relations Area.
- Maximo Camacho & Matías José Pacce, 2018. "Forecasting travellers in Spain with Google’s search volume indices," Tourism Economics, , vol. 24(4), pages 434-448, June.
- 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.
- Pietro Giorgio Lovaglio & Mario Mezzanzanica & Emilio Colombo, 2020. "Comparing time series characteristics of official and web job vacancy data," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(1), pages 85-98, February.
- Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
- 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.
- 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.
- 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.
More about this item
Keywords
keyword; Google; forecasting; nowcasting; tourism;All these keywords.
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
NEP fields
This paper has been announced in the following NEP Reports:- NEP-ICT-2012-04-03 (Information and Communication Technologies)
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bde:opaper:1203. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ángel Rodríguez. Electronic Dissemination of Information Unit. Research Department. Banco de España (email available below). General contact details of provider: https://edirc.repec.org/data/bdegves.html .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.